NWDAF analytics for improving network energy saving and energy efficiency

Abstract

A NWDAF-based analytics comprising a consumer NF, e.g. AF, PCF or NEF, requests or subscribes to analytics from the NWDAF and provides input information by invoking either Nnwdaf_AnalyticsSubscription_Subscribe or Nnwdaf_AnalyticsInfo_Request. The NWDAF subscribes to service data from an EMF by invoking Nemf_EventExposure_Subscribe service, from an SMF by Nsmf_EventExposure_Subscribe. A N4 session event is triggered. N4 related input data is provided by a UPF to the NWDAF via a SMF, or a UPF directly provides to the NWDAF. The NWDAF subscribes to input data from a OAM according to the data collection principles from the OAM, including data collection from a MDAS, from a AF by invoking Nnef_EventExposure_Subscribe or Naf_EventExposure_Subscribe service, from a UPF by invoking Nupf_EventExposure_Subscribe service, from a AMF using Namf_EventExposure_Subscribe service. The NWDAF subscribes to load of NF instances by using Nnrf_NFManagement_NFStatusSubscribe, the NWDAF derives requested analytics, in the form of energy consumption / efficiency related statistics / predictions or both, the NWDAF provides the requested energy related analytics to the consumer NF, using either Nnwdaf_AnalyticsInfo_Request response or Nnwdaf_AnalyticsSubscription_Notify, if the consumer NF subscribes to energy related analytics, when the NWDAF produces new analytics, it notifies the newly-produced analytics to the consumer NF.

Description

BACKGROUND Certain examples of the present disclosure provide various techniques relating to NWDAF-based analytics for improving network energy saving and energy efficiency, for example within 3rd Generation Partnership Project (3GPP) 5th Generation (5G) New Radio (NR) and NR-based relay networks. General description of energy usage, energy consumption and energy efficiency of 3GPP systems Currently, there are more than 110 countries committed to a net zero emissions target by 2050. What the Paris Agreement attempts to uphold is making sure global temperatures stay within 2°C by 2100, but preferably closer to 1.5°C. The motivation of reducing the energy emissions and increasing the energy efficiency of the telecommunications sector is more urgent than before. Also considering the price of energy is going up and the increasing traffic load of telecommunications systems, mobile network operators are keen to optimize the costs of ongoing operations (opex). Energy-saving measures in network operations are necessary for NR radio equipment and other components of telecommunications systems. Compared to previous generations, 5G NR offers a significant energy-efficiency improvement in its first release (3GPP Rel-15), i.e. cell activation / deactivation overXn / X2 / F1 interfaces via coordination between peer eNB / gNBs, sparser RS and SS signals, Ultra Reliable Low Latency Communications (URLLC), Central Unit (CU) I Distributed Unit (DU) architecture and MR-DC, etc. However, based on the GSMA report ‘5G energy efficiencies: Green is the new black’ (https: / / data.gsmaintelligence.com / api-web / v2 / research-file-download?id=54165956&file=241120-5G-energy.pdf) published in 2020, network opex tends to account for around 25% of Verizon’s cost base, or 10% of revenue. In addition, over 90% of network costs are spent on energy, consisting mostly of fuel and electricity consumption. To further reduce energy consumption and improve the efficiency of 3GPP systems, in releases later than Rel-15, some of the working groups (WGs) in Radio Access Network (RAN), System Architecture (SA) and CT have completed or are developing mechanisms to increase energy saving or energy efficiency. To reduce the energy consumption of the RAN part, in Rel-18, the RAN WG started to study and specify techniques for network energy savings (RAN WID in RP-223540 / RP-230566 Sept. 2023). System architecture 5 (SA5) working groups, started their work on energy efficiency of the 5G system in Rel-16. In Rel-17 (TR28.813 - Study on new aspects of Energy Efficiency (EE) for 5G) and Rel-18 (TR28.913), SA5 extended its scope from RAN only to the whole 5G system. The specified techniques are documented in TS28.310 ‘Management and orchestration; Energy efficiency of 5G’ and the corresponding Key Performance Indicators (KPIs) and measurements related to Energy Efficiency (EE) are documented TS28.552 ‘Management and orchestration; 5G performance measurements’ and TS28.554 ‘Management and orchestration; 5G end to end Key Performance Indicators (KPIs)’. SA1 working groups are currently working on the potential requirements and solutions in Rel-19 Energy Efficiency as a Service criteria (acronym: EnergyServ). This topic will be 100% completed by TSG 102 (Dec, 2023). The outcome of the study phase is documented in TR22.882 - Study on Energy Efficiency as service criteria). Some of the specified SA1 stage 1 requirements, e.g. maximum energy credit, could be down-streamed to SA2 for further stage 2 work. Existing and ongoing work in 3GPP SA1 working groups In the previous releases of NR (earlier than Rel-19), the studies concentrated more on how to satisfy user experience and to try to achieve energy efficiency at the same time. The use cases and solutions basically concern enhancements within the 3GPP network. For example, requirements for energy efficiency have been introduced by SA1 to clause 6.15 of TS22.261 as a fundamental 5G system requirement. However, those requirements are more focused on the optimization of UE battery life, based on network configuration and control, including UEs using small rechargeable and single coin cell batteries. The verticals (diverse industry sectors' service providers) and customers have no approach to enhance or improve the energy efficiency for the whole system. SA1 working groups have completed a study on energy efficiency as a service in Rel-19 (TR22.882), which enables users to select energy efficiency criteria based on request and some network performance parameters is needed. Therefore, in some scenarios, e.g. satellite and terrestrial convenience scenario, users or operators could choose I request a way to satisfy both user experience and energy efficiency. At the same time, the network could also deploy more efficient strategies, i.e. energy-efficient network resource allocation and scheduling. The SA1 work on energy efficiency as a service criteria mainly focuses on: • Defining and supporting energy efficiency criteria as part of a communication service to users and application services. • Providing information exposure on systematic energy consumption or level of energy efficiency to vertical customers. The conclusions of this study have been captured in the 5G system requirements specification, TS 22.261. These requirements might be addressed by SA2. SA2 working groups are studying potential solutions to accomplish or satisfy the corresponding SA1 requirements. The consolidated conclusions include but are not limited to the following (in clause 6 of TR22.882 and clause 6.15a of TS22.261): • Subject to operator’s policy, the 5G system shall support subscription policies and means to enforce the policy that define a maximum energy consumption rate for services without QoS criteria; • The 5G network shall support a means to define maximum energy consumption rate with specific granularities (which include subscriber granularity, network slice granularity). • Subject to operator’s policy, the 5G system shall support subscription policies that define a maximum energy credit limit for services. The maximum energy credit limit could be used to control the services. • Charging related requirements, i.e. subject to operator’s policy, the 5G system shall support a means to associate energy consumption with charging information based on subscription policies. • The 5G system shall support different energy states of network elements and network functions and dynamic switching between different energy states. • For monitoring and measurement related to energy efficiency purposes, the 5G network shall support energy consumption monitoring at per network slice and per subscriber granularity, the 5G system shall be able to acquire energy consumption information of network functions serving this third party, the 5G system shall be able to acquire a ratio of renewable energy used to provide a dedicated communication service to this third party on a periodic basis, the 5G system shall be able to acquire energy efficiency information (e.g., including estimated carbon emissions) related to a subscriber based on the subscriber’s data volume over a specific period of time, the operator’s network energy consumption, and the carbon intensity of the operator’s network. Existing and ongoing work in 3GPP RAN working groups To improve energy saving I efficiency and reduce operation expense of NR systems, in Rel-15 and later releases, RAN3 working groups introduced energy saving for intra- and intersystem, as described in clause 15.4 of TS38.300. This function allows deployment of capacity boosters which provide extra capability on top of basic coverage. Different from a cell that provides basic coverage, the capacity booster cells could be turned on and off by the NR-RAN node or the O&M autonomously, i.e. based on the load of the cell. The activation status or request of the capacity booster cell will be interacted over an Xn interface between corresponding NG-RAN nodes. The O&M-involved energy saving was specified by SA5 and documented in clause 5.1.3.3 of TS28.310. In Rel-18, considering the significant operational cost in the radio part, RAN approved the topic - ‘Network energy savings for NR’ in order to optimize energy consumption and energy efficiency of the radio part. The objectives of RAN working groups include but are not limited to the following: • Specifying SSB-less SCell operation for inter-band CA for FR1 and co-located cells. • Specifying enhancement on cell DTX / DRX mechanism including alignment of cell DTX / DRX and UE DRX in Radio Resource Control RRC_CONNECTED mode, and inter-node information exchange on cell DTX / DRX. • Improving energy efficiency or reducing energy consumption via spatial and power domain optimization, i.e. enhancements on CSI and beam management related procedures. • Specifying mechanism(s) to prevent legacy UEs camping on cells adopting the Rel-18 NES techniques, if necessary. • Specifying CHO procedure enhancement(s) in case source / target cells in NES mode. • Specifying inter-node beam activation and enhancements on restricting paging in a limited area. • Specifying corresponding RRM / RF core requirements, if necessary, for the above features. For the above RAN work, 5GC are not involved into either decision making on energy saving and energy efficiency enhancement or capacity booster cell activation and deactivation or configuration in the current standards. Existing and ongoing work in 3GPP SA5 working groups 3GPP SA5 working groups started work on ‘Energy efficiency of 5G’ since Rel-16. In Rel-16, SA5 working groups focused on the energy efficiency and Energy Saving (ES) of mobile networks. In Rel-17, the SA5 working groups extended the scope from RAN part only to the whole 5G system. EE KPIs have been defined for the 5G core network, network slices etc. SA5 work focuses on OA&M, i.e. defining mechanisms to collect measurements from the 5G Network Functions (NFs) via OA&M standardized APIs. Performance of network slices has been defined per type of network slice, namely for enhanced Mobile Broadband (eMBB), URLLC and massive Internet of Things (MIoT), whereas user plane traffic volumes have been considered to define the performance of the 5GC. Measuring Energy Consumption (EC) of Physical Network Functions (PNF) has been defined by ETSI EE, however measuring EC of Virtualized Network Functions (VNF) was blank. In Rel-17, SA5 working groups have defined a method to estimate EC, based on the estimated energy consumption of the underlying virtual compute resource instance(s), i.e. Virtual Machine(s) (VM). Currently, SA5 working groups are still working on Rel-18 energy efficiency of 5G. On top of Rel-17, in Rel-18, SA5 working groups are working on more accurate virtual CPU usage measurements from ETSI NFV MANO which could be used to estimate the EC of virtual machines, new use cases for ES in the whole 3GPP system, considerations on digital sobriety, etc. In future releases, some of the parameters and measurement techniques I metrics may be further enhanced by the SA2 working groups to support system level energy saving and efficient operation. SA5 working groups also introduced Management Data Analytics (MDA) assisted ES in clause 7.2.4 and clause 8.4.4 of TS28.104. The MDA assisted ES is achieved by activating an ES mode of a NR capacity booster cell or 5GC NFs (e.g. UPF etc.). By considering ES policy setup by operators, a Management Data Analytics Service (MDAS) producer is able to provide energy saving recommendations to a service consumer to assist with energy saving decisionmaking. For example, an MDAS procedure may provide an output which indicates where energy efficiency issues (e.g. high-energy consumption, low-energy efficiency) exist in a system and a cause of the energy efficiency issues based on the request of a consumer. An MDAS producer may also provide analysis related to energy saving to an MDAS consumer. The MDAS consumer may take the outputs of MDAS-based analytics into account to determine some simple energy saving decisions. After the outputs have been considered, the MDAS producer may start evaluating and further analyzing network management data to optimize the outputs. In order to generate energy saving recommendations the MDAS may obtain measurement data defined in TS28.552 and / or collect network analysis data from a Network Data Analytics Function (NWDAF), e.g. observed service experience related network data analytics. However, outputs of an MDAS-based energy saving analysis is limited to some qualitative analysis, i.e. the NR cells or NFs where energy efficiency issues occurred or potentially occur. The MDAS-based analytics cannot provide quantitative outputs to consumers, i.e. maximum energy consumption. Furthermore, a consumer of the MDAS-based energy saving analysis could be a NWDAF, AFs etc. Other 5GC NFs (i.e. UPF, PCF, etc.) cannot use the outputs of the MDAS for energy saving or energy efficiency improvement without any enhancement. Consideration of energy saving and enhancement of energy efficiency in SA2 Considering how energy is one of the most significant sources of operations costs for Mobile Network Operators (MNOs), there has been increasing work in 3GPP on improving energy efficiency and energy saving and reducing energy consumption of 5GS. In the above clauses, the existing work related to EE, ES and EC in other 3GPP working groups has been reviewed. From a network perspective, previous solutions studied how to optimize energy consumption by adapting the network itself, e.g. activating and deactivating parts of the network. Such changes to the topology and components of the network could be either transparent to the network architecture or have implications with the architecture, e.g. reselection of proper network functions. Standardization work on enhancement for energy efficiency and energy saving as service criteria has not been introduced to SA2 before Rel-19. As mentioned above, stage 1 requirements for energy as a service criteria have been identified by SA1 working groups in the FS_EnergyServ study whose conclusions are captured in the EnergyServ feature. Some of the SA1 requirements need to be addressed by SA2, as SA2 introduces new functionality. The goal of SA1 energy efficiency is to provide the same services in a more efficient manner, i.e. the services could be provided in an energy-aware manner with considering energy use control as service criteria, functional requirements including an ability to control energy use based on operator policies such as 'energy credit limits' and 'maximum energy usage rate' applying to services provided to a UE or group of UEs. Also, SA plenary has issued a 3GPP-wide recommendation on considering energy efficiency as an important design criterion for technical solutions which 3GPP defines in its specifications (see SP-211621). Therefore, SA2 working groups have decided to investigate options for improved system behaviour aimed at energy saving and energy efficiency in Rel-19. Currently, SA2 working groups are still discussing scope and objectives. The SID of SA2 work has been approved in the plenary meeting in SP-231192 (September 2023), including: • WT #1. Study potential framework for network energy consumption exposure. This will include whether and what information is exposed, how it is exposed (e.g. charging) and at what granularity e.g. at RAN level, Core Network (CN) level, network slice level, UE level, PDU session level, and / or QoS flow level. Additionally, whether and how renewable energy or carbon emission information for such granularities can be exposed by an MNO will be studied. • WT #2. Study enhancement for subscription and policy control to enable network energy savings as service criteria. • WT #3. Study 5GS enhancements (e.g. energy usage adjustment for NF from CN aspect, energy saving related decision making, NF selection leveraging NF energy states) for network energy saving including 5GC(NFs) and NG-RAN interactions, analytics, etc. Impacts on the UE are not ruled out e.g. for scenarios specified in TR22.882 by SA1 EnergyServ. NWDAF-based analytics provide statistics and predictions of the output parameters based on a request from a consumer. The NWDAF can provide information at a granularity related to an individual UE session. It is important to note that the MDAS mechanism cannot do this. In the EnergyServ work, there is interest in supporting an energy related service policy that is at a finer granularity than 'the entire network'. This means there will be a need for information in the 5G system to enforce the energy related service policy that cannot currently be addressed by existing mechanisms. As described in WT#3 in the SID of Feasibility Study on 5GS Enhancement for Energy Efficiency and Energy Saving (SP-231192), leveraging analytics could support the 5GS to enhance energy saving and improving energy efficiency. However, in the current 5GS specification, there is no NWDAF-based analytics which could assist the 5GS with reducing energy consumption and improving energy efficiency directly. In order to support 5GS Enhancement for Energy Efficiency and Energy Saving as service criteria, energy consumption and energy efficiency related analytics should be specified in R19 with the introduction of new analytics and / or enhancing existing analytics in TS23.288. Otherwise, there is no means to provide energy consumption (i.e. maximum energy credit specified by SA1) and energy efficiency (i.e. energy efficiency of different types of network slices) related outputs / recommendations to the 5G core (5GC). The above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with respect to the present invention. SUMMARY It is an aim of certain examples of the present disclosure to address, solve and / or mitigate, at least partly, at least one of the problems and / or disadvantages associated with the related art, for example at least one of the problems and / or disadvantages described herein. It is an aim of certain examples of the present disclosure to provide at least one advantage over the related art, for example at least one of the advantages described herein. Aspects, advantages, and salient features of the disclosure will become apparent to those skilled in the art from the following detailed description taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 illustrates exemplary procedures for a NWDAF to produce NWDAF-based analytics used to assist a communications network to support energy saving and to improve energy efficiency in the network; Figure 2 illustrates exemplary procedures for a NWDAF to derive energy related analytics; Figure 3 illustrates an exemplary method in a communications network of producing NWDAF-based analytics of energy-related aspects of the network and using the analytics to support energy saving and energy efficiency in the network, and Figure 4 is a block diagram of an exemplary NWDAF that may be used in certain examples of the present disclosure. DETAILED DESCRIPTION The following description of examples of the present disclosure, with reference to the accompanying drawings, is provided to assist in a comprehensive understanding of the present invention, as defined by the claims. The description includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made. The following examples are applicable to, and use terminology associated with, 3GPP 5G. However, the skilled person will appreciate that the techniques disclosed herein are not limited to these examples or to 3GPP 5G, and may be applied in any suitable system or standard, for example one or more existing and / or future generation wireless communication systems or standards. The skilled person will appreciate that the techniques disclosed herein may be applied in any existing or future releases of 3GPP 5G NR or any other relevant standard. For example, the functionality of the various network entities and other features disclosed herein may be applied to corresponding or equivalent entities or features in other communication systems or standards. Corresponding or equivalent entities or features may be regarded as entities or features that perform the same or similar role, function, operation or purpose within the network. For example, the functionality of an IAB node in the examples below may be applied to any other suitable type of entity performing functions of a network node. The skilled person will appreciate that the present invention is not limited to the specific examples disclosed herein. For example: • The techniques disclosed herein are not limited to 3GPP 5G. • One or more entities in the examples disclosed herein may be replaced with one or more alternative entities performing equivalent or corresponding functions, processes or operations. • One or more of the messages in the examples disclosed herein may be replaced with one or more alternative messages, signals or other type of information carriers that communicate equivalent or corresponding information. • One or more further elements, entities and / or messages may be added to the examples disclosed herein. • One or more non-essential elements, entities and / or messages may be omitted in certain examples. • The functions, processes or operations of a particular entity in one example may be divided between two or more separate entities in an alternative example. • The functions, processes or operations of two or more separate entities in one example may be performed by a single entity in an alternative example. • Information carried by a particular message in one example may be carried by two or more separate messages in an alternative example. • Information carried by two or more separate messages in one example may be carried by a single message in an alternative example. • The order in which operations are performed may be modified, if possible, in alternative examples. • The transmission of information between network entities is not limited to the specific form, type and / or order of messages described in relation to the examples disclosed herein. To satisfy extremely high data rate requirements, the 3GPP 5G NR standard utilises communication frequencies in a relatively high range, from 30 GHz to 300 GHz, corresponding to wavelengths in the millimetre (mm) range (mmWave communication). Such mmWave communication provides a large available bandwidth and high transmission speeds. However, problems with mmWave communication include severe signal path loss and low penetration, resulting in a relatively short transmission range. This in turn requires a greater density of base stations deployment. Certain examples of the present disclosure provide a network or wireless communication system comprising a first network entity and a second network entity according to any example, embodiment, aspect and / or claim disclosed herein. Certain examples of the present disclosure provide a computer program comprising instructions which, when the program is executed by a computer or processor, cause the computer or processor to carry out a method according to any example, embodiment, aspect and / or claim disclosed herein. Certain examples of the present disclosure provide a computer or processor-readable data carrier having stored thereon a computer program according to the preceding examples. Certain examples of the present disclosure may be provided in the form of an apparatus / device / network entity configured to perform one or more defined network functions and / or a method therefor. Such an apparatus / device / network entity may comprise one or more elements, for example one or more of receivers, transmitters, transceivers, processors, controllers, modules, units, and the like, each element configured to perform one or more corresponding processes, operations and / or method steps for implementing the techniques described herein. For example, an operation / function of X may be performed by a module configured to perform X (or an X-module). Certain examples of the present disclosure may be provided in the form of a system (e.g. a network) comprising one or more such apparatuses / devices / network entities, and / or a method therefor. For example, in the following examples, a network may include one or more IAB nodes. It will be appreciated that examples of the present disclosure may be realized in the form of hardware, software or a combination of hardware and software. Certain examples of the present disclosure may provide a computer program comprising instructions or code which, when executed, implement a method, system and / or apparatus in accordance with any aspect, claim, example and / or embodiment disclosed herein. Certain embodiments of the present disclosure provide a machine-readable storage storing such a program. The same or similar components may be designated by the same or similar reference numerals, although they may be illustrated in different drawings. Detailed descriptions of techniques, structures, constructions, functions or processes known in the art may be omitted for clarity and conciseness, and to avoid obscuring the subject matter of the present disclosure. Certain examples of the present disclosure provide, in a communications network, a method of producing NWDAF-based analytics of energy-related aspects of the network and using the analytics to support energy saving and energy efficiency in the network. In certain examples, using the analytics to support energy saving and energy efficiency in the network comprises consumers of the network using the analytics to any of determine, control, switch, modify energy-related network operation strategies, determine energy consumption, EC, and energy efficiency, EE, of a service of the network, determine allowed energy credit, energy consumption of transmitting a traffic volume to one or more user equipment, UE, of the network. In certain examples, producing NWDAF-based analytics of energy-related aspects of the network comprises the NWDAF collecting energy-related input data from one or more network entities. In certain examples, the network entity comprises an operations, administration and maintenance, 0AM, entity and the energy-related input data collected from the 0AM comprises any of a time window in which data is collected, predictions of traffic load trend of one or more cells, energy saving recommendations for NG-RAN, energy saving recommendations for 5GC, energy saving recommendations for one or more NF of the network, number of QoS flows, resource usage of one or more NF of the network, energy consumption at any of 5GC, NF, Network Slice, gNB or NG-RAN level, identifiers corresponding to the energy consumption, energy efficiency at any of 5GC, NF, Network Slice, gNB or NG-RAN level, identifiers corresponding to the energy efficiency, 5GC energy consumption obtained by summing energy consumption of all network functions of the 5GC, NF energy consumption obtained by summing energy consumption of any of one or more PNF of the NF, one ormore VNF of the NF, network slice energy consumption obtained by summing energy consumption of one or more network functions of the network slice, gNB energy consumption obtained by summing energy consumption of all network functions of the gNB, NG-RAN energy consumption obtained by summing energy consumption of gNB of the NG-RAN, identifiers of any of 5GC, NF instance, NF set, slice, RAN node, NG-RAN energy efficiency, network slice energy efficiency, number of UEs, ratio of renewable energy in the total energy supply, coefficient of carbon, carbon emission. In certain examples, the network entity comprises an application function, AF, and the energy-related input data collected from the AF comprises any of service data, a time window in which data is collected, an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, a downlink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, an uplink and downlink an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC. In certain examples, the network entity comprises a network repository function, NRF, and the energy-related input data collected from the NRF comprises any of NF status, NF-related load data, QoS monitoring data, number of UEs of at least a part of the network, QoS flow data, PDU session data. In certain examples, the network entity comprises a session management function, SMF, and the energy-related input data collected from the SMF comprises any of QoS monitoring data, a time window in which data is collected, an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, a downlink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, an uplink and downlink an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, number of QoS flows, number of PDU sessions. In certain examples, the network entity comprises a user plane function, UPF, and the energy-related input data collected from the UPF comprises any of QoS monitoring data, a time window in which data is collected, an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, a downlink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, an uplink and downlink an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, number of QoS flows, number of PDU sessions. In certain examples, the network entity comprises a management data analytics service / function, MDAS / MDAF, and the energy-related input data collected from the MDAS / MDAF comprises any of a timestamp associated with collected input data, EnergyEfficiencyProblematicObject indication of cells or NFs where energy efficiency issues may occur, EnergyEfficiencyProblemType indication of type of energy efficiency issues, TrafficLoadTrend predictions in a certain time period, RANEnergySavingRecommendations, CNEnergySavingRecommendations. In certain examples, the network entity comprises an access and mobility management, AMF, and the energy-related input data collected from the AMF comprises any of a number of UEs, a maximum number of UEs registered to the AMF, a mean number of UEs registered to the AMF, a number of subscribers per slice of the network. In certain examples, the network entity comprises an energy monitoring and management function, EMF, and the energy-related input data collected from the EMF comprises any of energy monitoring and management data, energy consumption and corresponding IDs at any of 5GC, NF, Network Slice, gNB or NG-RAN level, energy efficiency and corresponding IDs at any of 5GC, NF, Network Slice, gNB or NG-RAN level, 5GC energy consumption obtained by summing energy consumption of all network functions of the 5GC, NF energy consumption obtained by summing energy consumption of one or more PNF and / or one or more VNF of the NF, network slice energy consumption obtained by summing energy consumption of one or more network functions of the network slice, gNB energy consumption obtained by summing energy consumption of one or more network functions of the gNB, NG-RAN energy consumption obtained by summing energy consumption of gNB of the NG-RAN, identifiers comprising any of a NF ID, a NF set ID, a S-NSSAI, a gNB ID, identifiers of any of 5GC, NF instance, NF set, slice, RAN node, NG-RAN energy efficiency, network slice energy efficiency. In certain examples, the analytics of energy-related aspects of the network produced by the NWDAF comprise one or more recommendations for energy-related information comprising any of reducing network energy consumption, improving network energy efficiency, improving network energy saving. In certain examples, the analytics of energy-related aspects of the network produced by the NWDAF comprise calculations of energy-related information comprising any of network energy consumption information, network renewable energy consumption information, network energy efficiency information, network carbon emission information. In certain examples, the analytics of energy-related aspects of the network produced by the NWDAF comprise statistics of energy-related information comprising any of network energy consumption information, network renewable energy consumption information, network energy efficiency information, network carbon emission information. In certain examples, the analytics of energy-related aspects of the network produced by the NWDAF comprise predictions of energy-related information comprising any of network energy consumption information, network renewable energy consumption information, network energy efficiency information, network carbon emission information. In certain examples, the analytics of energy-related aspects of the network produced by the NWDAF comprise network service quality information associated with the energy-related information comprising any of traffic volume data, traffic rate data, packet delay data. In certain examples, the analytics of energy-related aspects of the network produced by the NWDAF comprise any of one or more specified analytics target periods, one or more time slots within an analytics target period within which the energy-related information is provided, identifiers of one or more specified network granularities, energy-related information provided for one or more specified network granularities and one or more specified analytics target periods, maximum I minimum I average I variance of energy consumption for one or more specified network granularities during an analytics target period, maximum I minimum / average I variance of renewable energy consumption for one or more specified network granularities during an analytics target period, maximum I minimum I average / variance of energy efficiency for one or more specified network granularities during an analytics target period, maximum / minimum I average / variance of carbon emission for one or more specified network granularities during an analytics target period, energy credit for one or more specified network granularities during an analytics target period, an energy credit limit for one or more specified network granularities during an analytics target period. In certain examples, the analytics of energy-related aspects of the network produced by the NWDAF comprise any of traffic volume used to derive energy-related information for one or more specified network granularities during an analytics target period, traffic / packet rate used to derive energy-related information for one or more specified network granularities during an analytics target period, traffic latency I packet delay associated with energy related information for one or more specified network granularities during an analytics target period, traffic rate I packet rate used to derive energy-related information for one or more specified network granularities during an analytics target period, one or more energy saving recommendation for any of a 5GC, NG-RAN of the network over an analytics target period. In certain examples, the analytics of energy-related aspects of the network produced by the NWDAF comprise any of identifiers of any of one or more network gNB associated with energy consumption, EC, / energy efficiency, EE, one or more network RAN node associated with EC I EE, one or more NF associated with EC I EE, one or more NF instance associated with EC / EE, one or more NF set associated with EC / EE, QoS requirements used to derive EC / EE, QoS characteristics used to derive EC I EE, 5QI of traffic used to derive EC / EE, recommended QoS requirements associated with EE I EC, QoS characteristics associated with EE I EC, 5QI of the traffic associated with EE I EC, PCC rules used to derive EC I EE, control policies used to derive EC I EE, recommended PCC rules associated with EE I EC, control policies associated with EE I EC. In certain examples, the one or more specified analytics target periods are specified by a consumer of the network and the one or more specified network granularity are specified by a consumer of the network and comprise any of PLMN level, RAN level, core network level, network slice level, UE level, PDU session level, QoS flow level. In certain examples, the NWDAF-based analytics are produced at the one or more specified granularities by considering percentages of total resources of the granularity. In certain examples, NWDAF-based analytics of renewable energy are produced by collecting a ratio of renewable energy in a total energy supply and a coefficient of carbon. In certain examples, the analytics of energy-related aspects of the network are produced by the NWDAF according to request information from a consumer of the network comprising any of one or more analytics IDs, one or more targets of analytics reporting comprising any of an optional entity, an optional resource, a single entity, a single resource, any entity or entities of a group of entities, any resource or resources of a group of resources, one or more network slice levels, a NF instance, a NF set level, a PDU session level, a QoS flow level, a set of QoS flow levels, UE level, a single UE, single SUPI / GPSI UE, a group of UEs, analytics filter information, comprising any of DNN, S-NSSAI, application ID, one or more NF instance IDs, one or more NF set IDs, one or more QoS requirements, one or more QoS characteristics, one or more 5Qls, a list of requested analytics subsets, one or more areas of interest, an analytics target period indicating a time period over which statistics or predictions are requested, a notification correlation ID that is included in a subscription, a notification target address that is included in a subscription, preferred level of accuracy of analytics, one or more reporting thresholds, a list of analytics subsets, preferred granularity of energy-related information comprising any of PDU session level, QoS flow level, application level. In certain examples, a procedure for producing NWDAF-based analytics comprises: 1. a consumer NF, e.g. AF, PCF or NEF, requests or subscribes to analytics from the NWDAF and provides input information by invoking either Nnwdaf_AnalyticsSubscription_Subscribe or Nnwdaf_Analyticslnfo_Request, 2a-2b. the NWDAF subscribes to service data from an EMF by invoking Nemf_EventExposure_Subscribe service, 2c. the NWDAF subscribes to service data from an SMF by invoking Nsmf_EventExposure_Subscribe (Event ID, SUPI(s) or Application ID), 2d. a N4 session event is triggered, 2e-2f. N4 related input data is provided by a UPF to the NWDAF via a SMF, 2f 1. Instead of step 2e-2f, a UPF directly provides the requested N4 related input data to the NWDAF, 2g-h. the NWDAF subscribes to input data from a 0AM according to the data collection principles from the QAM, including data collection from a MDAS, 2i-j. the NWDAF subscribes to service data from a AF by invoking Nnef_EventExposure_Subscribe or Naf_EventExposure_Subscribe (Event ID = energy related information, Application ID, Event Filter information, Target of Event Reporting) service, 2k-l. the NWDAF subscribes to service data from a UPF by invoking Nupf_EventExposure_Subscribe (Event ID = energy related information, Application ID, Event Filter information, Target of Event Reporting) service, 2m-n. the NWDAF subscribes to service data from a AMF using Namf_EventExposure_Subscribe service, 2o-p. the NWDAF subscribes to load of NF instances by using Nnrf_NFManagement_NFStatusSubscribe, 3. the NWDAF derives requested analytics, in the form of energy consumption and energy efficiency related statistics or predictions or both, 4. the NWDAF provides the requested energy related analytics to the consumer NF, using either Nnwdaf_Analyticslnfo_Request response or Nnwdaf_AnalyticsSubscription_Notify, depending on the service used in step 1, 5-7. if the consumer NF subscribes to energy related analytics at step 1, when the NWDAF produces new analytics, it notifies the newly-produced analytics to the consumer NF. Certain examples of the present disclosure provide a NWDAF of a communications network which produces analytics of energy-related aspects of the network according to methods of the present disclosure. Terminology Energy consumption / usage: as defined in clause 3.1 of TR23.700-66, the amount of energy which is utilized to achieve a specific system purpose expressed in Joule (J) or Watthour (Wh) (see TS28.310 and TS28.554). In this disclosure, this is the energy consumed by a network (i.e. RAN node, OAM, 5GS, 5GC NF(s), AF) and / or UEs. Note, 5GC NFs may be realized via virtualized network functions, physical network functions, a combination of virtualized network functions and physical network functions. Maximum energy credit: the amount of energy (consumption) for transmission of one or more services. In this disclosure, if the energy consumption I usage reaches a maximum energy credit, a network may take actions on the transmission of the one or more services, i.e. the one or more services might be cut off or a charge rate of a service may increase. Energy efficiency: as defined in clause 3.1 of TR23.700-66, the relation between a useful output and energy consumption (see TS 28.310 and TS 28.554). In this disclosure, energy efficiency could be the relation between the useful output and energy / power consumption, this KPI / parameter can be used to evaluate energy performance, i.e. energy performance of network entities or network systems. As defined in clause 3.1 and clause 5.3 of ETSI ES 203 228 and TS 28.554, mobile network data energy efficiency (EEmn, dv) is the ratio between a data volume (DVmn) and energy consumption (ECmn) when assessed during the same time frame. EEmn,dvis expressed in bit / J. The energy efficiency can be calculated in different granularities or for different entities. For example, if the energy efficiency is for NG-RAN data, as defined in clause 6.7.1 of TS28.554, the KPI shows the mobile network data energy efficiency in operational NG-RAN as data volume divided by energy consumption of considered network elements. The data volume (kbits) is obtained by measuring an amount of Downlink (DL) I Uplink (UL) Packet Data Conversion Protocol (PDCP) SDU bits of the considered network elements over the measurement period. For split-gNBs, the data volume is calculated per interface (F1-U, Xn-U, X2-U). The energy consumption (kWh) is obtained by measuring the PEE.Energy of the considered network elements over the same period of time. The samples are aggregated at the NG-RAN node level. The 3GPP management system responsible for the management of the gNB (single or multiple vendor gNB) shall be able to collect PEE measurement data from all PNFs in the gNB, in the same way as the other PM measurements. Based on the above formula, energy efficiency can be calculated in slice granularity, the generic network slice energy efficiency KPI is: . Il i-1- ixr^i performance of network slice (P„A Generic network slice EE KPI =----------------------------- Energy Consumption of network slice (EC^) The performance of network slice (Pns) is defined per type of network slice, the energy consumption of network slice (ECns) is defined independently from any type of network slice. The energy efficiency of different types of network slice are defined, for example, as follows. The energy efficiency of a eMBB network slice is Pns = DVmn, where DVmn or Pns is obtained by summing UL and DL data volumes at N3 interface(s) of the network slice. The unit of this energy efficiency KPI is bit / J. The energy efficiency of a LIRLLC network slice is Pns = 1 / (T2e2.mn), where T2e2.mn is the network slice mean latency, which is defined as the average end-to-end User Plane (UP) latency of the network slice, and where the average end-to-end user plane latency for one S-NSSAI. The unit of this energy efficiency KPI is (0.1ms * J)-1. The energy efficiency of an MIoT network slice is Pns = Nmmtc, where Nmmtc is the maximum number of subscribers registered to the network slice. The unit of this energy efficiency KPI is user / J. As defined in TS28.554, the generic 5GC energy efficiency KPI could be: Useful Output of 5GC (U sefulOutput5GC) Generic 5GC EE KPI = — ----------------—:— Energy Consumption of 5GC (EC5GC) The Useful Output of 5GC (UsefulOutputscc) is the useful output of a 5GC. It can be defined differently, depending on which 5GC network functions are considered. The Energy Consumption of 5GC (ECsgc) is the energy consumption of a 5GC. A latency based metric is the inverse ratio of the end-to-end user plane latency and the energy consumed by the mobile network: EEmn.us expressed in s'1 / J. In this disclosure, the energy related parameters I measurements I classes include the (maximum I average / minimum I variance of) energy consumption I energy usage / energy credit (limit) I energy efficiency related parameters I measurements I classes. In this disclosure, the different granularities I levels include, for example, any of PLMN level, RAN level, core network level, network slice level, UE level, PDU session level, QoS flow level. In this disclosure, the different (network) entities / resources / configurations include, for example, one or more of PLMN, NF, gNB, RAN node, slice, NF, UE, PDU session, QoS flow. In this disclosure, energy consumption represents any of maximum energy consumption I energy usage, minimum energy consumption I energy usage, average energy consumption I energy usage. Maximum energy credit limit: Definition of subscription is in TS21.905 maximum energy credit limit: a policy establishing an upper bound on the quantity of energy used by a 5GS to provide services to a specific subscriber. In this disclosure, this could be a limit of a total amount of energy consumption for best-effort services I services without QoS criteria, as defined in clause 6.15a.2.1 of TS26.221, or a maximum energy credit limit associated with a policy (i.e. charging policy, energy usage I efficiency related policy) or operating consequences (i.e. required QoS for service transmission, operating the service using alternative QoS), etc. In embodiments of the present disclosure, a NWDAF of a communications network may produce energy related NWDAF-based analytics which may be used to assist the network to support energy saving and to improve energy efficiency in the network. In embodiments of the present disclosure, a NWDAF of a 5GS may produce energy related NWDAF-based analytics which may be used to assist the 5GS to support energy saving and / or improve energy efficiency in the 5GS. In embodiments of the present disclosure, a NWDAF of a 5GS may produce NWDAF-based energy saving analytics and NWDAF-based energy efficiency analytics which may be used to assist the 5GS in reducing energy consumption, improving I maximizing energy efficiency, or both in the 5GS. In embodiments of the present disclosure, the energy related NWDAF-based analytics may be specified by detailing any of one or more functions of the NWDAF-based analytics, request information from a consumer, filter information, NWDAF input information, NWDAF-based analytics outputs, NWDAF-based analytics procedures. In embodiments of the present disclosure, the NWDAF of the communications network may produce one or more NWDAF-based analytics outputs. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may be in the form of statistics. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may be in the form of predictions. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may be in the form of statistics and predictions. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may be in the form of statistics of any of maximum energy consumption, average energy consumption, minimum energy consumption, energy consumption variance, maximum energy usage, average energy usage, minimum energy usage, energy usage variance, maximum energy credit, average energy credit, minimum energy credit, energy credit variance, maximum energy efficiency, average energy efficiency, minimum energy efficiency, energy efficiency variance in different granularities. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may be in the form of predictions of any of maximum energy consumption, average energy consumption, minimum energy consumption, energy consumption variance, maximum energy usage, average energy usage, minimum energy usage, energy usage variance, maximum energy credit, average energy credit, minimum energy credit, energy credit variance, maximum energy efficiency, average energy efficiency, minimum energy efficiency, energy efficiency variance in different granularities. The one or more NWDAF-based analytics outputs can assist consumers to: • make decisions on 5GS15GC energy saving, reducing energy consumption, improving I maximizing energy efficiency at different levels I granularities, • determine energy related operation strategies which consider energy efficiency and energy consumption, which strategies may include any of traffic steering, scheduling, charging, optimisation of cost to provide a service data, entities selection, entities reselection, resources selection, resources reselection, UE selection, UE reselection, NF selection, NF reselection, network slice selection, network slice reselection, • determine, for example, any of allowed energy credit, allowed energy consumption, allowed energy cap, maximum energy credit, maximum energy consumption, maximum energy cap, determine any of acceptable required energy efficiency, lowest required energy efficiency at different granularities (i.e. energy consumption of transmitting a certain traffic volume to multiple UEs or a single I specific UE), expose information to an AF or 5GC NFs for determining service strategies which consider energy related aspects, i.e. the thresholds I requirements I number of network slices to transmit the corresponding services, • assist NWDAF consumers (i.e. Session Management Function (SMF)) with policy decision making and determining whether to enforce PCC rules / polices, based on (real-time / historical I predicted) energy consumption, energy policies, energy credit, energy cap, energy consumption rate, etc., • different granularities to a consumer to assist the consumer to switch I modify the strategies or policies, • provide / determine / modify I enforce I switch QoS requirements / 5GS control policies for data I service transmission considering energy related parameters, i.e. if consumers could be any of PCF, SMF, UPF, AF etc., • notify (real-time) energy consumption, energy efficiency, energy policies, energy credit, energy cap etc. at different levels to consumers; therefore, consumers can make energy related decisions taking these analytics into account, • recommendations of quality of service, entities I resources selection or other options considering energy consumption I energy efficiency of a 5GS, i.e. the QoS of service(s) which transmit traffic which consider allowed energy consumption I energy cap I energy credit / required energy efficiency etc. provided by the consumer. In embodiments of the present disclosure, NWDAF-based analytics outputs in real-time may be calculations of energy-related information to produce analytics outputs, such as energy consumption, energy policies, energy credit, energy cap, energy consumption rate, etc. The calculations may be based on, for example, formulas for the energy-related information to produce the analytics. The formulas may comprise summing or multiplexing collected energy-related information to determine the analytics outputs in real-time. These outputs may be different from analytics output predictions and analytics output statistics. In embodiments of the present disclosure, input information is provided to the NWDAF for producing the energy related NWDAF-based analytics. In embodiments of the present disclosure, the input information provided to the NWDAF may comprise input information collected from one or more network entities. The one or more network entities may, for example, comprise any of QAM, QAM including MDAS, MDAS / MDAF, AF, one or more 5GC NFs, UPF, SMF, NRF. In embodiments of the present disclosure, the input information may, for example, comprise any of traffic volume, delay, latency, packet rate of data transmission related measurements at different granularities, UE throughput, one or more QoS requirements, one or more QoS characteristics, one or more 5Qls associated with the energy efficiency measurements and energy consumption measurements, resource utilisation, one or more energy consumption measurements, one or more energy efficiency measurements, load of network entities at different granularities, load of resources at different granularities, load of configurations at different granularities, output of MDAS-based analytics related to energy saving, allowed energy credit, expected energy credit, allowed energy consumption, expected energy consumption, required energy efficiency, lowest energy efficiency. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may comprise any of statistics of energy consumption, statistics of energy usage, statistics of energy credit, statistics of energy efficiency. The statistics of energy consumption, statistics of energy usage, statistics of energy credit, statistics of energy efficiency may be over one or more analytics target period in the past at different levels, in a request or subscription for statistics of a consumer NF. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of prediction of energy consumption, prediction of energy usage, prediction of energy credit, prediction of energy efficiency. The prediction of energy consumption, prediction of energy usage, prediction of energy credit, prediction of energy efficiency may be over one or more analytics target period in the future at different levels, in a request or subscription for predictions of a consumer NF. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise energy consumption at a granularity of any of an entity, a resource, a configuration. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise energy usage at a granularity of any of an entity, a resource, a configuration. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise maximum energy credit limit at a granularity of any of an entity, a resource, a configuration. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise energy efficiency at a granularity of any of an entity, a resource, a configuration. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of average energy consumption associated with any of one or more network entities, one or more subscribers of a service that may consider energy as a service criteria, energy consumption variance associated with any of one or more network entities, one or more subscribers of a service that may consider energy as a service criteria, maximum energy consumption associated with an entity, minimum energy consumption associated with any of one or more network entities, one or more subscribers of a service that may consider energy as a service criteria. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of average energy efficiency associated with any of one or more network entities, one or more subscribers of a service that may consider energy as a service criteria, energy efficiency variance associated with any of one or more network entities, one or more subscribers of a service that may consider energy as a service criteria, maximum energy efficiency associated with any of one or more network entities, one or more subscribers of a service that may consider energy as a service criteria, minimum energy efficiency associated with any of one or more network entities, one or more subscribers of a service that may consider energy as a service criteria. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of average energy credit limit associated with any of one or more network entities, one or more subscribers of a service that may consider energy as a service criteria, energy credit limit variance associated with any of one or more network entities, one or more subscribers, maximum energy credit limit associated with any of one or more network entities, one or more subscribers, minimum energy credit limit associated with any of one or more network entities, one or more subscribers. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of statistics, predictions of any of consumption of renewable energy, generation of carbon emissions. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise IDs of any of entities used to derive energy related outputs, resources used to derive energy related outputs, configurations used to derive energy related outputs. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise a number of any of entities used to derive energy related outputs, resources used to derive energy related outputs, configurations used to derive energy related outputs. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise IDs of any of recommended entities associated with energy related outputs, recommended resources associated with energy related outputs, recommended configurations associated with energy related outputs. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise a number of any of recommended entities associated with energy related outputs, recommended resources associated with energy related outputs, recommended configurations associated with energy related outputs. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of recommended QoS requirements, recommended QoS characteristics, recommended 5QI associated with energy related outputs, corresponding QoS requirements, corresponding QoS characteristics, corresponding 5QI associated with energy related outputs. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of recommended data volume related fields associated with energy related outputs, recommended data rate related fields associated with energy related outputs, corresponding data volume fields associated with energy related outputs, corresponding data rate related fields associated with energy related outputs. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of recommended PCC rules related fields associated with energy related outputs, recommended PCC policy related fields associated with energy related outputs, corresponding PCC rules related fields associated with energy related outputs, corresponding PCC policy related fields associated with energy related outputs. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of an entity energy consumption ratio of total energy consumption, a resource energy consumption ratio of total energy consumption. These may help a consumer to allocate an energy consumption problem. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may, for example, comprise any of one or more energy consumption classes, one or more energy efficiency classes, high-energy related classes, medium-energy related classes, low-energy related classes, corresponding entities per class, corresponding resources per class, IDs of entities in each class, ratios of entities in each class, IDs of resources in each class, ratios of resources in each class. In embodiments of the present disclosure, the NWDAF of the communications network may produce one or more NWDAF-based analytics outputs comprising one or more energy saving related analytics outputs. The one or more energy saving related analytics outputs may be provided to a service consumer. The one or more energy saving related analytics outputs may be used to assist the network with reducing energy consumption and I or improving energy efficiency. In embodiments of the present disclosure, the NWDAF of the communications network may produce one or more NWDAF-based analytics outputs comprising one or more energy efficiency related analytics outputs. The one or more energy efficiency related analytics outputs may be provided to a service consumer. The one or more energy efficiency related analytics outputs may be used to assist the network with reducing energy consumption and I or improving energy efficiency. In embodiments of the present disclosure, the input information provided to the NWDAF may comprise energy saving related input information collected from one or more network entities. In embodiments of the present disclosure, the input information provided to the NWDAF may comprise energy efficiency related input information collected from one or more network entities. The network entities may, for example, be any of 5GC NFs, OAM, AF. A consumer may subscribe to analytics notifications from the NWDAF. This uses a Subscribe-Notify model. A consumer may request a single notification from the NWDAF. This uses a Request-Response model. In embodiments of the present disclosure, the one or more energy saving related analytics outputs and the one or more energy efficiency related analytics outputs may provide one or more energy saving related output parameters and one or more energy efficiency related output parameters in different granularities over different time periods. The one or more energy saving related output parameters and one or more energy efficiency related output parameters may be provided in accordance with a request of a consumer. The output parameters may, for example, comprise any of energy consumption at a RAN level, energy consumption at a core network level, energy consumption at a network slice level, energy consumption at a UE level, energy consumption at a PDU session level, energy consumption at a QoS flow level, energy efficiency at a RAN level, energy efficiency at a core network level, energy efficiency at a network slice level, energy efficiency at a UE level, energy efficiency at a PDU session level, energy efficiency at a QoS flow level. The output parameters may, for example, comprise any of energy consumption of a service, energy consumption of transmission of a piece of data, energy consumption of specified traffic volumes, energy consumption of transmitting one or more services to one or more UEs, energy consumption within a period of time. The one or more services may, for example, be transmitted to the one or more UEs via any of one or more PDU sessions, one or more QoS flows, one or more NFs. In embodiments of the present disclosure, the one or more energy saving related analytics outputs and the one or more energy efficiency related analytics outputs may provide any of one or more energy consumption related analytics outputs, one or more energy efficiency related analytics outputs for one or more services. The one or more services may be composed of one or more QoS flows, one or more network slices of a UE, a list / group of UEs. The UEs may, for example, be UEs involved in any of one or more group communication services, one or more Al ML federated learning services, one or more MBS services. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may be in accordance with one or more request service quality requirements. The one or more request service quality requirements may, for example, comprise any of one or more QoS requirements, one or more required QoS characteristics, one or more 5Qls. The NWDAF-based analytics outputs may, for example, comprise any of maximum energy consumption, average energy consumption, minimum energy consumption, maximum energy credit, average energy credit, minimum energy credit. In embodiments of the present disclosure, NWDAF-based analytics related to energy saving and / or energy efficiency may provide analytics outputs relating to energy saving and / or energy efficiency to one or more consumers. In embodiments of the present disclosure, the NWDAF-based analytics may provide analytics outputs comprising end-to-end energy consumption for transmitting one or more services between one or more UEs and one or more AFs. The end-to-end energy consumption may comprise any of an overall energy consumption of a 5GC for transmitting one or more services, an energy consumption of one or more entities deployed in transmitting one or more services within a 5GS, an energy consumption of one or more resources deployed in transmitting one or more services within a 5GS. In embodiments of the present disclosure, the NWDAF may estimate any of energy consumption, energy efficiency, energy credit limit, maximum energy rate in different granularities based on input information comprising any of transmitted data volume, transmitted traffic volume, to be transmitted data volume, to be transmitted traffic volume. If traffic volume is given, any of energy efficiency, energy consumption of transmitting an amount of traffic within a period of time could be estimated. The amount of traffic may belong to one or more services. The one or more services may be one or more specific types of services, for example, one or more low latency service, one or more deterministic networking services, one or more high density services, one or more MBS services. Therefore, the energy consumption and energy efficiency for different types of service / traffic can be derived. In embodiments of the present disclosure, the NWDAF may estimate any of energy consumption, energy efficiency, energy credit limit, maximum energy rate in different granularities based on input information comprising data rate of traffic. If an average data rate of traffic is given, the energy consumption and energy efficiency for transmitting the traffic at a certain data rate could be estimated. The average data rate of traffic may, for example, be any of per QoS flow, per PDU session level. In embodiments of the present disclosure, the NWDAF may estimate any of energy consumption, energy efficiency, energy credit limit, maximum energy rate in different granularities based on input information comprising any of historical energy consumption data, historical energy efficiency data. If the historical energy consumption of an entity is given, a prediction and / or statistics of energy consumption and energy efficiency at different scenarios of QoS requirements of traffic transmission, different number of UEs in different granularities may be determined. In embodiments of the present disclosure, the NWDAF may estimate any of energy consumption, energy efficiency, energy credit limit, maximum energy rate in different granularities based on input information comprising any of a load of one or more entities, a load of one or more resources. The load may, for example, be any of a load of a UE, a load of a beam, a load of a cell, a load of a NG-RAN node, a load of a 5GC NF, a load of a network slice, a load of a 5GC. In embodiments of the present disclosure, the NWDAF may estimate any of energy consumption, energy efficiency, energy credit limit, maximum energy rate in different granularities based on input information comprising any of an activation time of one or more entities, an activation time of one or more resources, an activation time of one or more PDU sessions, an activation time of one or more QoS flows, a number of corresponding active PDU sessions, a number of corresponding active QoS flows. If an energy consumption and / or an energy efficiency to be determined is coarser than a UE granularity, a number of UEs belonging to or served by any of a network slice, a NG-RAN node, a 5GC NF, a UPF, a SMF may be considered to calculate the energy consumption of any of a network slice, a RAN node, a 5GC NF, a5GC. In embodiments of the present disclosure, the NWDAF may estimate any of energy consumption, energy efficiency, energy credit limit, maximum energy rate in different granularities based on input information comprising any of transmission delay, transmission latency. The energy efficiency of a network slice may be determined by any of transmission delay, transmission latency. The network slice may be a URLLC network slice. In embodiments of the present disclosure, the NWDAF may calculate energy related information in finer granularities by considering a number of any of network slices, NFs, PDU sessions, QoS flows, UEs, per PDU session level in which the energy consumption is the total energy consumption divided by the number of PDU sessions. The number of PDU sessions may be the number of successfully established PDU sessions. In embodiments of the present disclosure, the NWDAF may calculate energy related information in finer granularities by considering a number of any of network slices, NFs, PDU sessions, QoS flows, UEs, per QoS flow level in which the energy consumption is the total energy consumption divided by the number of QoS flows. The number of QoS flows may be the number of successfully established QoS flows. In embodiments of the present disclosure, the NWDAF may calculate energy related information in finer granularities by considering a number of any of network slices, NFs, PDU sessions, QoS flows, UEs, per UE level in which the energy consumption is the total energy consumption divided by the number of UEs. The number of UEs may be the number of any of active UEs, registered UEs, online UEs. In embodiments of the present disclosure, NWDAF-based energy saving related analytics and energy efficiency related analytics may provide analytics outputs comprising any of statistics, predictions for one or more areas of interest requested by a consumer. The one or more areas may be any of a geographical area, an energy efficiency area. The energy efficiency area may be an area within which a service is provided in an energy efficient manner. Area information may be presented by any of a list of cells, a list of cell IDs, one or more TAs, one or more gNBs, one or more RAN nodes, geographical information, beam information. In embodiments of the present disclosure, one or more energy consumption rates, one or more energy consumption bands, one or more energy consumption classes, one or more energy efficiency rates, one or more energy efficiency bands, one or more energy efficiency classes may be assigned to any of one or more entities, one or more resources. The assignment may, for example, be performed by any of a network operator, an energy supplier, a 5GC NF, a gNB, a RAN node or an AF. The assignment may comprise indicating any of one or more thresholds, an upper boundary, a lower boundary of each energy related class. The one or more entities may be categorised into any of one or more energy rates, one or more energy bands, one or more energy classes based on any of entity energy consumption, entity energy efficiency. The one or more resources may be categorised into any of one or more energy rates, one or more energy bands, one or more energy classes based on any of resource energy consumption, resource energy efficiency. For example, based on any of energy consumption, energy efficiency, any of a RAN, a core network, a network slice, a UE level, a PDU session, a QoS flow may be categorised into any of one or more high-energy consumption classes, one or more high-energy efficiency classes, one or more medium-energy consumption classes, one or more medium-energy efficiency classes, one or more low-energy consumption classes, one or more low-energy consumption classes, using the one or more thresholds of the classes. Any of an energy efficiency band, an energy consumption band may be used to let any of a consumer, a network operator understand energy efficiency I energy consumption status at different granularities. Therefore, an MNO can identify a root cause of energy consumption and energy efficiency problems. The MNO may then take one or more actions to reduce energy consumption and improve energy efficiency. In embodiments of the present disclosure, the NWDAF of the communications network may produce one or more NWDAF-based analytics outputs associated with a time window both in the past and in the future. In embodiments of the present disclosure, the one or more NWDAF-based analytics outputs may be associated with one or more operation scenarios. The one or more operation scenarios may comprise any of different QoS requirements for traffic transmission, PCC rules, PCC control policies, a number of UEs involved. Therefore, a consumer NF or a MNO can understand different network energy efficiency and energy consumption associated with different policies, QoS requirements, different number of UEs / NFs I QoS flows etc. Therefore, based on the analytics outputs, a consumer NF or a MNO can derive the most energy efficient way to provide the service to subscribers. In embodiments of the present disclosure, consumers of the analytics outputs may include any of an AF, one or more 5GC NFs, an OAM. The one or more 5GC NFs may, for example, comprise any of a PCF, a NEF, an AMF, a SMF, a UPF, a NRF. In embodiments of the present disclosure, a consumer may indicate in a request one or more analytics IDs. The one or more analytics IDs may comprise any of one or more energy consumption related analytics IDs, one or more energy saving related analytics IDs, one or more energy efficiency related analytics IDs, one or more existing analytics IDs. In embodiments of the present disclosure, a consumer may indicate in a request one or more targets of analytics reporting. The one or more targets of analytics reporting may comprise any of an optional entity, an optional resource, a single entity, a single resource, any entity or entities of a group of entities, any resource or resources of a group of resources. The resources may comprise any of a SUPI for a UE, a GPSI for a UE, a QFI for a QoS flow. In embodiments of the present disclosure, a consumer may indicate in a request analytics filter information. The analytics filter information may, for example, comprise any of DNN, S-NSSAI, application ID, one or more NF instance IDs, one or more NF set IDs, one or more NF types, one or more QoS flow IDs, one or more QFIs, one or more QoS requirements, one or more QoS characteristics, one or more 5Qls, one or more PDU session IDs, a list of requested analytics subsets. The analytics filter information may, for example, comprise one or more PLMN IDs. The one or more PLMN IDs may identify one or more target PLMNs. These may one or more PLMNs from which analytics are requested in roaming cases. The analytics filter information may, for example, comprise one or more areas of interest. The one or more areas of interest may restrict a scope of energy saving related analytics and energy efficiency related analytics to a provided area of interest. The one or more areas of interest may be any of an energy efficiency area, a list of cells, a list of one or more TAs. The analytics filter information may, for example, comprise any of a maximum number of objects, a maximum number of NFs, a maximum number of NF instance IDs, a maximum number of NF set IDs, a maximum number of network slices, a maximum number of UEs, a maximum number of SUPIs, a maximum number of PDU sessions, a maximum of PDU session IDs, a maximum number of QoS flows, a maximum number of QFIs. The analytics filter information may, for example, comprise any of a maximum number of NFs, a maximum number of slices, a maximum number of UEs, a maximum number of PDU sessions, a maximum number of QoS flows, configured for transmitting one or more services. The one or more services may be transmitted between one or more UEs and one or more AFs. The one or more services may be transmitted from the one or more UEs to the one or more AFs and / or from the one or more AFs to the one or more UEs. The analytics filter information may, for example, comprise any of an uplink data volume, a downlink data volume, a roundtrip data volume. These may indicate a specific data volume transmitted between different entities, e.g. the traffic volume from a UE to a AF and / or from an AF to a UE, the traffic volume between a UE and a base station (e.g. a RAN node), the traffic volume between a UE and a 5GC (i.e. a UPF), the traffic volume between a RAN node and a UPF, the traffic volume between a CU and a DU, the traffic volume between a 5GC (i.e. a UPF) and an AF. The analytics filter information may, for example, comprise a type of services. The type of services may, for example, comprise MBS, low latency service, high density service, time I deterministic networking service, GBR, GBR-critical traffic. The analytics filter information may, for example, comprise any of one or more QoS requirements, one or more 5Qls, one or more individual QoS characteristics. The one or more individual QoS characteristics may, for example, include any of a packet delay budget, a packet error rate, a default maximum data burst, a default averaging window. In embodiments of the present disclosure, a consumer may indicate in a request an analytics target period. This may indicate a time period over which statistics or predictions are requested. In embodiments of the present disclosure, a consumer may indicate in a request a notification correlation ID and a notification target address are included in a subscription. In embodiments of the present disclosure, a consumer may indicate in a request any of a preferred level of accuracy of analytics, a preferred number of samples, variance of the NWDAF-based analytics. In embodiments of the present disclosure, a consumer may indicate in a request a preferred order of results fora list of energy consumption, energy usage, energy credit, and energy cap in different granularities. The preferred order of results may comprise an ordering criterion comprising "energy consumption I energy usage / energy credit / energy cap ". The preferred order of results may an ordering criterion comprising any of an ascending order, a descending order. In embodiments of the present disclosure, a consumer may indicate in a request one or more reporting thresholds. These may indicate conditions on a level to be reached for respective analytics subsets. In embodiments of the present disclosure, the input information provided to the NWDAF may comprise energy saving related input information collected from one or more network entities. In embodiments of the present disclosure, the input information provided to the NWDAF may comprise energy efficiency related input information collected from one or more network entities. The network entities may, for example, be any of AF, OAM, MDAS / MDAF, one or more 5GC NFs. In embodiments of the present disclosure, the input information provided to the NWDAF may be collected from an OAM. The input information collected from the OAM may, for example, comprise traffic volume related input information. The NWDAF may use the traffic volume input information to derive one or more energy saving related analytics outputs and one or more energy efficiency related analytics outputs. In embodiments of the present disclosure, the traffic volume related input information may, for example, comprise any of a number of data packets, a number of octets of data packets, a volume of data packets between any of a RAN and a UE, a RAN and a UPF. This includes any of UL data packets, DL data packets, roundtrip data packets, outgoing data packets, ingoing data packets. The NWDAF may use the traffic volume related input information to determine traffic volume between different entities. The NWDAF may then determine energy consumption of the different entities for data transmission within any of a time window, a time duration, a time period. Corresponding time window I time duration I time period for collection of traffic volume related input information may also be provided by the OAM. Alternatively, corresponding time window I time duration / time period for collection of traffic volume related input information may be provided by a consumer. The corresponding time window / time duration / time period may be provided by a consumer in a request. The request may comprise any of a Nnwdaf_AnalyticsSubscription_Subscribe request, a Nnwdaf_Analyticslnfo_Request request. The input information may be collected for one or more time windows I time durations I time periods / time intervals. The one or more time windows I time durations I time periods / time 5 intervals may be requested by any of an analytics consumer, determined by one or more providers of input information. For the same input information collected in different time intervals, variation of the input information in different time intervals may be derived. The variation information helps to compare levels of the input information in one time interval from other time intervals and therefore help identify if there is any significant contribution I change 10 of any of UE, corresponding entity of the input information, corresponding service by which the input information is collected. The variation I comparison of the input information in different time intervals also helps to inspect whether a UE I service is operating or not, then one can have some confidence in the average. In embodiments of the present disclosure, the input information collected from an OAM may 15 be as set out in Table 1. Table 1 input data I service data I information collected from an OAM related to traffic volume and delay / latency for analytics on energy saving and energy efficiency. Input Information Source Description Timestamp Time window(s) / time duration(s) / time period(s) OAM OAM A time stamp associated with the collected information. The collected input information includes the parameters collected from an OAM in this table. The time window(s) / time duration(s) / time period(s) associated with the collected input information, i.e. a window / period / duration of time collecting input information, or a start time and end time of data collection, or points of times (start time and end time) to calculate the input information by the OAM. When the start time is T1 and the end time is T2, the collected input information (i.e. traffic volume) was measured between T1 and T2. The collection information was measured within a period / duration. The length of the duration could be (T2-T1), or exact numbers of time length (i.e. 10s, 10ms etc.). UE ID or list of UE IDs OAM List ofSUPI(s) orGPSI(s). Average or Distribution of UL / DL UE throughput in gNB UL / DLPDCP Data Volume OAM OAM Average / distribution of RAN UE throughput in uplink direction / downlink direction, as defined in clause 5.1.1.3 of TS28.552. This is measured at a gNB by the OAM. It refers to the UL / DL data volume transmitted between the gNB and UE(s). Per QoS level (mapped 5QI or QCI in NR option 3) and per supported S-NSSA, per PLMN ID, per (active) BWP. For gNB non-split case: UL / DL Cell / X2 interface / Xn interface PDCP SDU Data Volume, as defined in clause 5.1.2.1 ofTS28.552, • the Data Volume (amount of PDCP SDU bits) in the uplink / downlink delivered to a PDCP layer, • this is measured at a gNB obtained by counting the number of bits entering the NG-RAN PDCP layers. Data volume / number of octets / number of data packets from UPF to OAM RAN, or from RAN to UPF, or between RAN and UPF Number of octets / number of data packets for (PSA) UPF e2e uplink / downlink / OAM overall delay UL / DL packet OAM delay between PSA UPF and UE For gNB split case: UL / DL Cell / X2 interface / Xn interface PDCP PDU Data Volume, as defined in clause 5.1.3.6 ofTS28.552, • the Data Volume (amount of PDCP PDU bits) in the downlink delivered from GNB-CU to GNB-DU, • this is obtained by counting the number of DL PDCP PDU bits sent to gNB-DU. Per PLMN ID and per QoS level (mapped 5QI) and per S-NSSAI. The unit is Mbit. The data volume / octets / number of data packets between RAN and UPF is the sum of those from UPF to RAN and from RAN to UPF, as defined in clause 5.4.1 ofTS28.552. This is measured at UPF by the OAM. Per S-NSSAI and / or per QoS level (5QI). The number of GTP data packets from RAN to UPF (incoming), or from UPF to RAN (outgoing), or between RAN and UPF (incoming + outgoing): the number of GTP data PDUs on the N3 interface which have been accepted and processed by the GTP-U protocol entity in UPF on the N3 interface, or generated by the GTP-U protocol entity on the N3 interface, or the sum of the above two. The number of octets of GTP data packets from RAN to UPF (incoming), or from UPF to RAN (outgoing), or between RAN and UPF (incoming + outgoing): the number of octets of GTP data PDUs on the N3 interface which have been accepted and processed by the GTP-U protocol entity in UPF on the N3 interface, or outgoing GTP data packets on the N3 interface which have been generated by the GTP-U protocol entity on the N3 interface, or the sum of the above two. Data volume of GTP data packets per QoS level from RAN to UPF (incoming), or from UPF to RAN (outgoing), or between RAN and UPF (incoming + outgoing): data volume of the incoming GTP data packets per QoS level which have been accepted and processed by the GTP-U protocol entity, or outgoing GTP data packets per QoS level which have been generated by the GTP-U protocol entity, or the sum of the above two, on the N3 interface, per QoS level (5QI). GTP Data Packets and / or volume on N9 interface, as defined in clause 5.4.4.2 ofTS28.552. This is measured at UPF by the OAM. Per S-NSSAI. Number of data packets for PSAUPF: incoming and / or outgoing data packets, number of GTP data PDUs received and / or received on the N9 interface by the PSA UPF. Number of octets of data packets for PSAUPF: incoming and / or outgoing data packets, number of octets of GTP data PDUs received and / or received on the N9 interface by the PSA UPF. e2e UL / DL / overall packet delay between the PSA UPF and the UE for a network slice, as described in clause 6.3.1.8 ofTS28.554. Could be average values. The average / distribution of UL / DL / overall packet delay between PSA UPF and UE as captured in clauses 5.4.9.1 and 5.4.9.2 in TS28.552. In embodiments of the present disclosure, the input information collected from a OAM may comprise input information related to energy consumption. The NWDAF may use the input information related to energy consumption to derive statistics and predictions of energy saving related outputs and energy related outputs for consumers. The input information related to 5 energy consumption may be based on estimation and measurement. In embodiments of the present disclosure, the input information related to energy consumption collected from a OAM may be as set out in Table 2. Table 2 input data / service data I information collected from an OAM related to energy consumption I energy efficiency for analytics on energy saving and energy efficiency. 10 Input Information Source Description Timestamp OAM A time stamp associated with the collected input information. The collected input information includes the parameters collected from an OAM in this table. Time window(s) / time duration(s) / time period(s) NF Energy Consumption OAM The time window(s) / time duration(s) / time period(s) associated with the collected input information, i.e. the period / duration of time of the collected input information, or a start time and an end time of the collection, or points of times (start time and end time) to calculate the input information by the OAM. When the start time is T1 and the end time is T2, the collected input information (i.e. traffic volume) was measured between T1 and T2. The collection information was measured within a period / duration. The length of the duration could be (T2-T1) or exact numbers of time length (i.e. 10s, 10ms etc.). Energy consumption of a 5G network function. The OAM may provide the energy consumption of one or more NFs to the NWDAF based on a request. The NWDAF collects the energy consumption of one or more NFs based on subscriptions or request from a consumer. As defined in clause 6.7.3.1 ofTS28.554, the energy consumption of a NF is the sum of the energy consumption of PNF(s) and / or VNF(s) which compose the NF. The unit is J. : The energy consumption of PNF(s) may be measured by the OAM directly. The energy consumption of VNF(s) is an estimation. It is the sum of all the energy consumption of the constituent virtualize network function components (VNFC). The energy consumption of each VNFC is estimated based on the energy consumption of the virtual compute resource instance on which the VNFC runs. The input information may be an average, minimum or maximum of this parameter. NF ID(s), NF OAM / The NF ID(s), NF Set IDs, NF types associated with the above collected NF Set IDs, NF types 5GC NFs energy consumption. Indicate the NF energyiconsumption were collected for which NF(s). 5GC Energy Consumption OAM Energy consumption of the 5G Core Network. This is obtained by summing the energy consumption of all the network functions (ECNF) that compose the 5G core network as defined in clause 6.7.3.2 of TS28.554. The input information may be an average, minimum or maximum of this parameter. Network Slice Energy Consumption OAM Energy consumption of the network slice. It is obtained by summing the energy consumption of a II the network functions (ECNF) that compose the network slice as defined in clause 6.7.3.3 of TS28.554. The unit of this KPI is J. The network slice EC = EC of RAN network slice subnet +EC of a Transport Network (TN) network slice subnet + EC of a 5GC network slice subnet. The input information may be an average, minimum or maximum of this parameter. NF Resource OAM The usage of assigned virtual resources currently in use for specific NF Usage Network Slice Resource Utilization Ratio instance(s) (mean usage of virtual CPU, memory, disk) as defined in clause 5.7 ofTS28.552. Virtualised resource utilization of network slice instance, as defined in clause 6.4.2 of TS28.554. The utilization of virtualised resource (e.g. processor, memory, disk) that are allocated to a network slice. It is obtained by the usage of virtualised resource (e.g. processor, memory, disk) divided by the system capacity that is allocated to the network slice. It is a percentage and the type is Ratio. S-NSSAI OAM / 5GC NFS The slice ID(s) associated with the above collected network slice energy consumption and / or network slice resource utilization ratio. Indicate the NF energyiconsumption was collected for slice(s). NG-RAN Energy Consumption OAM Energy consumption of the NG-RAN. It is obtained by summing the energy consumption of all the gNBs that constitute the NG-RAN, as defined in clause 6.7.3.4 of TS28.554. The unit of this KPI is J. The input information may be an average, minimum or maximum of this parameter. NG-RAN ID(s), OAM / The NG-RAN ID(s) / NG-RAN name(s) associated with the above collected NG-RAN Name(s) 5GC NFs NG-RAN energy consumption. Indicate the NG-RAN energy consumption was collected for which NR-RAN(s). gNB EC OAM The energy consumption of the gNB. It is obtained by summing up the energy consumption of all the network functions that constitute the gNB, as defined in clause 6.7.3.4.2 of TS23.554. The input information may be an average, minimum or maximum of this parameter. gNB ID(s), gNB OAM / The gNB ID(s) / gNB name(s) associated with the above collected gNB energy Name(s) 5GC NFs consumption. Indicate the gNB energy consumption was collected for which gNB(s). Energy efficiency OAM The energy efficiency (EE) include the EE of NR-RAN data, network slice (including eMBB, URLLC, mloT) and 5GC, as defined in clause 6.7.2 of TS28.554 and clause 6.1.2 ofTS28.310. NG-RAN ID(s), NG-RAN Name(s), S-NSSAI, Slice Types, PLMN ID OAM The IDs / name / any type of identifiers associated with the above EE. If the EE is the EE of a network slice, the S-NSSAI (slice ID) and the slice type are associated with this network slice for which the data EE is collected / calculated. In embodiments of the present disclosure, the input information collected from a OAM may be as set out in Table 3. Table 3 input data I service data I information collected from an OAM for analytics on energy saving and energy efficiency. Input Information Source Description Timestamp OAM A time stamp associated with the collected input information. The collected input information includes the parameters collected from an OAM in this table. Time window(s) / time duration(s) / time period(s) OAM See Table 1 or Table 2: QoS flow related OAM or May include: information, i.e. SMF, • in-session activity time for QoS flow, as defined in clause 5.1.1.13.2 of in-session activity time for QoS flow, number of QoS flows successfully establishment Number of QoS flows successfully created UPF TS28.552. The aggregated active session time for QoS flow in a cell, per QoS level measurement, • number of QoS flows successfully establishment, as defined in clause 5.1.1.13.3.2 ofTS28.552. The numberof QoS flows successfully established. The measurement is per QoS level and per S-NSSAI. • Number of QoS flows successfully created, as defined in clause 5.3.2.1.2 of TS 28.552. This measurement provides the number of QoS flows successfully created. This measurement is split into subcounters per S-NSSAI and subcounters per 5QI. • Or the Number of QoS flows successfully created / establishment of a PDU session / slice collected from UPF or SMF. • Or the number of the total qos flow belong to the SMF, 5QI, slice, PDU session 5ect., at the time point / within the time window when the measurement is collected PDU session OAM or May include: related SMF, • Number of PDU Sessions successfully setup, as defined in clause information, i.e. number of PDU sessions successfully setup, mean number of PDU sessions UPF 5.1.1.5.2 of TS28.552, provides the number of PDU sessions successfully setup by the gNB from AMF. This measurement is per S-NSSAI. • Mean numberof PDU sessions: mean number of PDU sessions of network and network slice instance, mean number of PDU sessions that are successfully established in a network slice. It is obtained by successful PDU session establishment procedures of SMFs which is related to the network slice. • Number of successful PDU session creations by the SMF, in clause 5.3.1.4 of TS 28.552. this is measurement provides the number of PDU sessions successfully created by the SMF. Each PDU session Successfully created is added to the relevant subcounter per S-NSSAI and the relevant subcounter per request type. • Or the Number of PDU session successfully created / establishment, i.e. a within slice, collected from UPF or SMF. • Or the number of the total pdu sessions belong to the SMF, 5QI, slice, etc., at the time point / within the time window when the measurement is collected. Number of OAM, May include: active UEs AMF, • Number of Active UEs, the mean and / orthe maximum number of active related information, i.e. number of UEs SMF UEs in the UL and / or DL, i.e. per cell, as defined in clause 5.1.1.23 of TS28.552. per PLMN ID and per QoS level (mapped 5QI and / or QCI in NR option 3) and per supported S-NSSAI. Number of OAM, May include: registered AMF, • Number of registered subscribers, the mean and / or the maximum subscribers related SMF number of registered state subscribers per AMF, as defined in clause 5.2.1 information, i.e. number of registered subscribers, maximum online subscribers of TS28.552. per S-NSSAI. Or the mean and / or maximum number of subscribers that are registered to a network slice instance. It is obtained by counting the subscribers in AMF that are registered to a network slice instance. • Maximum on-line subscribers: maximum on-line subscribers of network slice through AMF. The maximum number of subscribers in a period that are not only registered to a network slice but also established a PDU session related to a network slice. Subscribers also have a NAS signalling connection, as defined in clause 6.2.9 ofTS28.554. In embodiments of the present disclosure, the input information provided to the NWDAF may be collected from a MDAS / MDAF. In TS28.104, the energy saving analysis as defined in clause 7.2.4.1 and 8.4.4. In embodiments of the present disclosure, the input information collected from a MDAS / MDAF 5 may be as set in Table 4. Table 4 input data / service data I information collected from a MDAS / MDAF related to analytics on energy saving and energy efficiency. Input Information Source(s) Timestamp MDAF EnergyEfficiencyProblematicObject MDAF EnergyEfficiencyProblemType MDAF TrafficLoadTrends MDAF RANEnergySavingRecommendations MDAF CNEnergySavingRecommendations MDAF StatisticsOfCellsESState MDAF Description A time stamp associated with the collected input information. The collected input information includes the parameters collected in this table. Indication of NR cells or NFs where the energy efficiency issues occurred or potentially occur, as defined in 8.4.4 ofTS28.104. Indication of type of energy efficiency issues. The allowed value is one of the enumerated values: HighEnergyConsumption, LowEnergyEfficiency, other, unknown, as defined in 8.4.4 of TS28.104. The predictions of the trends of traffic load in a certain time period. The predictions include the traffic load of the issue cell(s) and neighboring cell(s). Defined in 8.4.4 of TS28.104. For ES on NR cells. It may contain a set of: - Recommended NR Cell (ES-Cell) to enter energy saving state. - Recommended candidate cells with precedence for taking over the traffic of the ES-Cell. - The time to enter and terminate the energy saving state, : - The load threshold to enter and terminate the energy saving state for the ES-Cell. This exists only in cases where RAN energy saving is supported. Defined in 8.4.4 of TS28.104. ForESonUPFs. It contains a set of: : - Recommended UPF (ES-UPF) to conduct energy saving. : - Recommended candidate UPFs with precedence for taking over the traffic of the ES-UPF. : - The time to conduct energy saving for the ES-UPF. This exists only in cases where CN energy saving is supported. Defined in 8.4.4 of TS28.104. The statistic result of current energy saving state of the cells at a certain time. In embodiments of the present disclosure, the input information provided to the NWDAF may be collected from an AF. In embodiments of the present disclosure, the input information provided to the NWDAF may be collected from one or more 5GC NFs. In embodiments of the present disclosure, the input information collect from an AF and / or one 5 or more 5GC NFs may be as set in Table 5. Table 5 input data / service data I information collected from an AF and / or one or more 5GC NFs for analytics on energy saving and energy efficiency. Input Information Application ID Source(s) AF Description Identifying the application providing the collected input information. 5GC NF Any 5GC The address(es) / identifier(s) of the 5GC NFs associated with the collected information / (list NFs from input information / serving the UE(s). of) NF Instance IDs, NF Set IDs, NF Types which the data is collected (i.e. UPF, SMF, PCF, etc.) The address(es) / ID(s) of SMF, UPF, etc. UE ID(s), list of AF, UPF, One or more UE IDs or list / group of the UE ID(s). UE IDs SMF, AMF The UE IDs could be SUPI(s) or GPSI(s). DNN SMF, UPF The DNN for which the PDU session is established. Timestamp AF or any 5GC NFs from which the data is collected A time stamp associated with the collected input information. The collected input information includes the parameters collected in this table. Time window(s) / time duration(s) / time period(s) / Measurement Period AF The time window(s) / time duration(s) / time period(s) associated with the collected input information, i.e. the period / duration of time of the collected input information, or a start time and an end time of the data collection, or points of time (start time and end time) to calculate the input information by theAF. Where the start time is T1 and the end time is T2, the collected input information (i.e. traffic volume) was measured between T1 and T2. The collection information was measured within a period / duration. The length of the duration! could be (T2-T1), or exact numbers oftime length (i.e. 10s, 10ms etc.). Data Volume UPF, SMF, Data volume UL / DL / both UL and DL (over all) over the measurement UL / DL / both UL and DL (overall) or AF period, average / peak / maximum value. If the information is collected from UPF or SMF: sum of data volume exchanged over the duration, this information could be : • per QoS flow; • per PDU session, i.e. per PDU session across all the QoS flow(s) established within this PDU session; • per UE, i.e. per UE across all applications of the UEs; • per application, i.e. per application across all the UEs receiving the service from: the application; • per UPF / SMF, i.e. per UPF across all the UEs, applications etc. If the data volume is collected from AF: sum of data volume exchanged at the AF during the period of observation. Throughput UPF, SMF, Throughput UL / DL / both UL and DL over the measurement period, average / UL / DL / both UL and DL (overall) orAF peak / maximum value. DL / UL Data Rate UPF UL / DL data rate, i.e. of a QoS flow. QoS SMF Identify QoS requirements / QoS characteristics / 5QI(s) of the one or more requirements / QoS characteristics / 5QI(s) and / or AF pieces of traffic flow / data flow of the corresponding service / service (to be transmitted). (Allowed / maximum) energy credit / energy cap / energy consumption / energy usage AF The allowance / maximum allowance of the energy consumed for a service: • by one or more NFs; • by one or more network slice; • by the 5GC / 5GS, etc., to transfer the one or more service(s) / data flow(s) / traffic volume(s) to one or more UEs, via one or more QoS flows and one or more PDU sessions. The energy consumption might be associated to different QoS / 5QI(s), i.e. the energy consumption to transfer the traffic with the required QoS or determined 5QI. The NWDAF may use the energy credit / allowance to determine the QoS and other parameters for data transmission. (Required / minimum) energy efficiency AF The (required / expected / minimum) EE for a service, might be the EE of a network slice, 5GC NF, 5GC etc. The NWDAF may use the energy credit / allowance to determine the QoS and other parameters for data transmission. Renewable Energy Consumption AF The (required / expected / minimum) renewable energy should be consumed for a service. The renewable EC could be in different levels. Carbon Emission AF The (required / expected / maximum) carbon emission could be generated for a service. The carbon emission could be in different levels. Number of UEs AF, AMF, UPF, SMF Mean, average, maximum, minimum number of UEs. The number of UEs involved in the service, on the network slice, served by the AMF / SMF / UPF, etc. UE Locations AMF or GMLC Location of the UE(s) needs to be selected via AMF if the application needs to be started at the same time. If the area of interest indicated by the AF is a finer granularity area than the cell level, the current location of the UE(s) needs to be selected via GMLC instead. End to end delay / latency / transmission time AF Delay / latency / transmission time between the UE and AF. This information could be (QoS flow) packet delay, the time for transmission of a piece of data / certain data volume, etc. QoS flow packet delay SMF, UPF The observed packetdelay for UL / DL / round trip directions between UE and PSA_UPF. PCC rules PCF The PCC rules associated with the data transmission. In embodiments of the present disclosure, the input information collected from one or more 5GC NFs may be as set out in Table 6. Table 6 input data / service data I information collected from one or more 5GC NFs related to NF load for analytics on energy saving and energy efficiency. Input Information Source(s) Description Timestamp NRF A time stamp associated with the collected input information. The collected input information includes the parameters collected in this table. 5GC NF information / (list of) NF Instance IDs, NF Set IDs, NF Types; NRF The address(es) / identifier(s) / types of the 5GC NFs associated with the collected input information. Time window(s) / time duration(s) / time period(s) / Measurement Period NRF The time window(s) / time duration(s) / time period(s) associated with the collected input information, i.e. the period / duration of time of the collected input information, or a start time and an end time of the data collection, or points of times (start time and end time) to calculate the information by the AF. Where the start time is T1 and the end time is T2, the collected input information (i.e. traffic volume) was measured between T1 and T2. The collection of input information was measured within a period / duration. The length of the duration could be (T2-T1), or exact numbers of time length (i.e. 10s, 10ms etc.). NF load / capacity NRF The load and / or capacity of specific NF instance(s) in their NF profile as defined perTS29.510. The NF load might be a dynamic load information, the load of the NF at the time (time stamp) when the load information is collected, average / maximum / minimum / variance of the load information over the measurement period.; NF Status NRF The status of specific NF instance(s) (registered, suspended, undiscoverable) as defined perTS29.510. Number of NFs NRF / AF The number of NFs (to be) involved / deployed to transfer the service data / serve all the UEs. The number of UPFs; / SMFs (one type of NF) or the number of all the NFs (all types of NF) deployed to transmit the data between AF(s) and UE(s). In embodiments of the present disclosure, the input information provided to the NWDAF may be collected from any of 5GC NF, NEF, AF to derive any of consumption of renewable energy, generation of carbon emission. The information may be provided by an energy supplier to an AF or NEF, or provisioned to a 5GC NF. 5 In embodiments of the present disclosure, the input information collected to derive any of consumption of renewable energy, generation of carbon emission may be as set out in Table 7. Table 7 input data I service data I information collected from 5GC NFs related to NF load for analytics on energy saving and energy efficiency Input Information Source(s) Description Timestamp AF / NEF A time stamp associated with the collected input information. The collected input information includes the parameters collected in this table. Weight / ratio of renewable energy in the total energy supply Coefficient of carbon emission Coefficient / weight ratio of renewable energy in the total energy supply. Used to derive the carbon emission. The statistically observed grams per milliwatthour for any use of energy in the country where the service is provided. 10 In embodiments of the present disclosure, the NWDAF supporting analytics on energy saving and energy efficiency may provide outputs to consumer NFs. The consumer NFs may, for example comprise any of AF, PCF, NEF, new energy function related 5GC NFs. The outputs provided by the NWDAF may be any of energy saving related statistics, energy efficiency 5 related statistics, energy saving related predictions, energy efficiency related predictions. The energy saving related statistics and energy efficiency related statistics are for performance in the past and may be over an analytics target period if configured. A gNB I RAN node may indicate an entire gNB in a non-split architecture, or a gNB-Cll / DU / CU-UPI CU-CP in a split architecture. 10 In embodiments of the present disclosure, the outputs provided by the NWDAF may be any of energy saving related statistics, energy efficiency related statistics as defined in Table 8. Note that, in this table (and other tables in the present disclosure), the notation ‘>’, '»’ and »>’ is used to indicate that the corresponding entry in the table relates to a previous entry in the table, e.g., a subset / attribute of that previous entry. For instance, referring to Table 8, the 15 “Ratio of entities” may be regarded as an attribute of “EC / EE / energy credit rate I band / class” data. Table 8 energy saving related statistics and energy efficiency related statistics. Output Description List of energy-related analytics (1... max) (NOTE 1) (NOTE: EE / EC is used to keep the name short) > Time slot entry (1..max) » Time slot start Including but not limited to statistics of energy consumption I energy usage, energy consumption / energy usage of renewable energy, (maximum) energy credit limit and EE at different levels / granularities etc. Definition of different granularities / levels and different entities / resources are specified as above. (List of) energy consumption I energy usage / energy credit and / or EE at core network level, RAN level, network slice level, UE level, PDU session level, QoS Flow level, etc. (List of) energy consumption / energy usage, energy consumption I energy usage of renewable energy, (maximum) energy credit limit and EE of one or more PLMN / NF / network slice I gNB I RAN node / UE / PDU session I QoS flow, etc. Maximum is the number of the entities / resources / configurations to derive the energy consumption / energy usage / energy credit and / or EE, i.e. the number of PLMN, NF, gNB, RAN node, slice, NF, UE, PDU session, QoS flow etc., if applicable or a time slot for the analytics. The energy consumption / energy usage, energy consumption / energy usage of renewable energy, (maximum) energy credit limit and EE, could be average / maximum / minimum / variance values. List of time slots during an analytics target period. The time slots within which the energy consumption / energy usage, energy consumption / energy usage of renewable energy, (maximum) energy credit limit and EE analytics is provided. Time slot start within an analytics target period. » Duration Duration of time slot. » UE location Indicates UE location information when the service(s) is delivered or the EE / EC is derived, i.e. energy efficiency area(s), list of cells / TAs, etc. » Application ID Identifiers of one or more applications in use during a time slot. The applications that provide service(s) via / to a 5GS during the time slot within which the EE / EC is derived. » DNN DNN(s) for PDU session(s) which contain QoS flow(s). » PLMN ID (One or more) IDs of the PLMN(s). The PLMN(s) are associated with the EC / EE. » S-NSSAI Identifiers of (recommended) network slice used to access the application(s). (One or more) IDs of the network slice associated with the EC / EE. » gNB / RAN node ID / name Identifiers of (recommended) gNB(s) / RAN node(s) associated with the EC / EE. » NF ID, NF Set ID, NF instance ID Identifiers of (recommended) NF, NF instance, NF set associated with the EC / EE. » UE ID or list of UE IDs Identifies a (recommended) UE or a group of (recommended) UEs associated with the EC / EE, e.g. a list of UEs for which the statistic applies. SUPI(s) orGPSI(s). The UE could be an individual UE, as requested by a consumer or UE(s) that are receiving / transmitting service data from / to / via a required application(s), DNN(s), service(s), network slice, NFs, PDU session or QoS flow belongs to, etc. » PDU session ID Identifiers of (recommended) PDU sessions associated with the EC, energy credit and EE. » QoS flow ID (QFI) Identifiers of (recommended)QoS flows associated with the EC / EE. » Number of PLMN / NF / gNB / RAN node / NF (sets) / slice / UE / PDU session / QoS flow (NOTE 1) » QoS / 5QI (NOTE 1) (Recommended) number of PLMN / gNB / RAN node / slice / NF / UE / PDU session / QoS flow to derive / associated with the statistics of the EC / EE. The number could be an average, maximum, minimum value. The average number of the UEs that belong to one or more slices. The QoS requirements, QoS characteristics, 5QI of the (UL / DL) traffic associated with / used to derive the corresponding EC / EE. This may include: packet delayibudget, packet error rate, default maximum data burst, default averaging window etc. : Or recommended QoS requirements, QoS characteristics, 5QI of the (UL / DL) traffic considering a required EC / EE given by a consumer / associated with statistics of the EE / EC. » PCC rules / control policies (NOTE 1) PCC rules / control policies associated with / used to derive the corresponding EC / EE. Or recommended PCC rules / control policies considering a required EC / EE given by a consumer / associated with statistics of the EE / EC. » Maximum / minimum / average / variance of EC The maximum / minimum / average / variance of EC consumed / generated by corresponding entities / resources at different levels. The EC of PLMN, RAN, core network, network slice, UE level, PDU session, and / or QoS flow. Or the EC of transmission of a service including the EC of all involved edentates. The ID / name of corresponding entities / resources are also correspondingly provided. » Maximum / minimum / average / variance of renewable EC or ratios of renewable EC in the total EC » Maximum / minimum / The maximum / minimum / average / variance of EC of renewable energy consumed / generated by corresponding entities / resources at different levels. Or the ratio of renewable energy in the total EC (the above parameter). The maximum / minimum / average / variance of carbon emission of different entities at different levels, i.e. for transmitting a service. average / variance of carbon emission » Maximum / minimum / average / variance EC of different NF related services Or The EC ratios of different NF related services within the EC of the corresponding NF » Maximum / minimum / average / variance .................... II® » Maximum energy credit (limit) » Traffic volume / throughput The maximum / minimum / average / variance EC of different NF related services, i.e. the EC of NF discovery, selection and reselection, subscription, notification etc. or The EC ratios of different NF related services within the EC of a corresponding NF, i.e. the EC ratios of NF discovery, selection and reselection, subscription, notification etc. within the total EC of the NF. i The maximum / minimum / average / variance of EE of the corresponding entities / resources at different levels. The upper bound on the quantity of energy used by the 5G system to provide services provided to a specific subscriber. The maximum energy credit of different entities at the corresponding different levels. Maximum / minimum / average / variance traffic volume / throughput associated with / used to derive statistics of the EE / EC / maximum energy credit. The traffic volume / throughput of different entities / resources, i.e. the traffic volume / throughput of one or more PLMN, NF, gNB, RAN node, slice, NF, UE, PDU session, QoS flow etc., i.e. the throughput of UE(s). » Traffic / data / packet rate Maximum / minimum / average / variance traffic rate / packet rate associated with statistics of the EE / EC. The traffic / data / packet rate of different entities / resources. »latency / packet delay Maximum / minimum / average / variance latency / packet delay associated to statistics of the EE / EC. i.e. the latency / packet delay of different entities / resources » EC ratio (O...max) (NOTE 1) The (list of) ratio / percentage of EC of the entities / resources of the total EC. Maximum is the number of the entities / resources, i.e. PLMN / gNB / RAN node / slice / NF / UE / PDU session / QoS flow, etc., if applicable. The EC ratio / percentage of a network slice (10J) of the total EC (100J), the EC ratio / percentage is 10J / 100J = 10%. The ID(s) of the entities / resources associated with the ratios are also provided. Using this, a consumer can estimate the percentage of the EC of each entity / resource of the total EC. » EC / EE / energy credit rate / band / class (O...max) (NOTE 1) List of groups of entities / resources classified by ranges of EE / EC related parameters. Maximum is the maximum number of the class, i.e. if there are 3 EE / EC classes (low-, medium-, and high-EE / EC class), the maximum is 3. Using this, a consumer can understand the energy class of each entity / resource and the ratios of the entities / resources in each energy class. »> Ratio of entities / resources per energy class The ratio / percentage of the entities / resources in the corresponding energy related class. The ratio / percentage of the network slices (10) of the total number of the network slices (100) used to derive the given EE / EC in the high-energy class. In this case, the ratio of entities 1 resources in the high-energy class is 10 / 100 = 10%. »Location information of an entity The location of one or more entities that are associated with energy efficiency or energy consumption. If the energy efficiency or energy consumption is for a UE, the location information of the entity is the UE location, Lei UE geographical location, cell ID, tracking area of the UE, beam information of the UE, etc. » Validity period The validity period within a time slot for the EC and EE related statistics as defined in clause 6.1.3 of TS28.288. » Spatial validity Area where the EC and EE related statistics apply. NOTE 1: Analytics subset that can be used in "list of analytics subsets that are requested", "Preferred level of accuracy per analytics subset" and "Reporting Thresholds". In embodiments of the present disclosure, the outputs provided by the NWDAF may be any of energy saving related predictions, energy efficiency related predictions as defined in Table 9. Table 9 energy saving related predictions and energy efficiency related predictions. Output Description Including but not limited to the predictions of energy consumption / energy usage, energy consumption / energy usage of renewable energy, (maximum) energy credit limit and EE at different levels / granularities etc. Definition of different granularities / levels and different entities / resources are specified above. (List of) energy consumption / energy usage / energy credit limit and / or EE at core List of energy-related analytics (1...max) (NOTE 1) (NOTE: EE / EC is used to keep the name short) network level, RAN level, network slice level, UE level, PDU session level, QoS flow level, etc. (List of) energy consumption 1 energy usage, energy consumption / energy usage of renewable energy, (maximum) energy credit limit and EE of one or more PLMN / NF / network slice / gNB / RAN node / UE / PDU session / QoS flow, etc. Maximum is the number of the entities / resources / configurations to derive an energy consumption / energy usage / energy credit limit and / or EE, i.e. the number of PLMN, NF, gNB, RAN node, slice, NF, UE, PDU session, QoS flow etc., if applicable, or a time slot for the analytics. The energy consumption / energy usage, energy consumption / energy usage of renewable energy, (maximum) energy credit limit and EE may be average / maximum / minimum / variance values. > Time slot entry (1..max) List of time slots during an analytics target period. The time slots within which energy consumption / energy usage, energy consumption / energy usage of renewable energy, (maximum) energy credit limit and EE analytics is provided. » Time slot start Time slot start within an analytics target period. » Duration. Duration of a time slot. » UE location Indicates UE location information when service(s) is delivered / or the EE / EC is derived, i.e. energy efficiency area(s), list of cells / TAs, etc. » Application ID Identifiers of one or more applications in use during a time slot. The applications that provide service(s) via / to a 5GS during a time slot within which the EE / EC is derived. » DNN DNN(s) for the PDU session(s) which contains a QoS flow(s). » PLMN ID (One or more) IDs of a PLMN(s). The PLMN(s) are associated with the EC / EE. » S-NSSAI Identifiers of (recommended) network slice used to access the application(s). (One or more) IDs of a network slice associated with the EC / EE. » gNB / RAN node ID / name Identifiers of (recommended) gNB(s) / RAN node(s) associated with the EC / EE. » NF ID, NF Set ID, NF instance ID Identifiers of (recommended) NF, NF instance, NF set associated with the EC / EE. » UE ID or list of UE IDs Identifies a (recommended) UE ora group of (recommended) UEs associated with the EC / EE, e.g. a list of UEs for which the statistic applies. SUPI(s) or GPSI(s). The UE may be an individual UE, as requested by a consumer or a UE(s) receiving / transmitting service data from / to / via requires appIication(s), DNN(s), service(s), network slice, NFs the PDU session or QoS flow belongs to, etc. » PDU session ID Identifiers of (recommended) PDU sessions associated with the EC, energy credit and EE. » QoS flow ID (QFI) Identifiers of (recommended) QoS flows associated with the EC / EE. » Number of PLMN / NF / gNB / RAN node / NF (sets) / slice / UE / PDU session / QoS flow (NOTE 1) » QoS / 5QI (NOTE 1) (Recommended) number of PLMN / gNB / RAN node / slice / NF / UE / PDU session / QoS flow to derive / associated with the statistics ofthe EC / EE. The number could be average, maximum, minimum values. The average number ofthe UEs belonging to one or more slices. The QoS requirements, QoS characteristics, 5QI ofthe (UL / DL) traffic associated with / used to derive corresponding EC / EE. This may include: packet delay budget, packet error rate, default maximum data burst, default averaging window etc. Or recommended QoS requirements, QoS characteristics, 5QI ofthe (UL / DL) traffic considering required EC / EE given by a consumer / associated with statistics ofthe EE / EC. » PCC rules / control policies (NOTE 1) The PCC rules / control policies associated with / used to derive the corresponding EC / EE. Or recommended PCC rules / control policies considering the required EC / EE given by a consumer / associated with statistics ofthe EE / EC. » Maximum / minimum / average / variance of EC The maximum / minimum / average / variance of EC consumed / generated by corresponding entities / resources at different levels. The EC of PLMN, RAN, core network, network slice, UE level, PDU session, and / or QoS flow. Or the EC of transmission of a service including the EC of all involved edentates. The ID / name of corresponding entities / resources are also correspondingly provided. » Maximum / minimum / average / variance of renewable EC or ratios of renewable EC in the total EC » Maximum / minimum / average / variance of Carbon emission » Maximum / minimum / average / variance EC of different NF related services Or EC ratios of different NF related services within the EC ofthe corresponding NF » Maximum / minimum / average / variance of EE » Maximum energy credit (limit) The maximum / minimum / average / variance of EC of renewable energy consumed / generated by corresponding entities / resources at different levels. Or the ratio of renewable energy in the total EC (the above parameter). The maximum / minimum / average / variance of carbon emission of different entities at different levels, i.e. for transmitting a service. The maximum / minimum / average / variance EC of different NF related services, i.e. the EC of NF discovery, selection and reselection, subscription, notification etc. or The EC ratios of different NF related services within the EC of a corresponding NF, i.e. the EC ratios of NF discovery, selection and reselection, subscription, notification etc. within the total EC ofthe NF. The maximum / minimum / average / variance of EE of corresponding entities / resources at different levels. The upper bound on the quantity of energy used by a 5G system to provide services provided to a specific subscriber. The maximum energy credit of different entities at corresponding different levels. » Traffic volume / throughput Maximum / minimum / average / variance traffic volume / throughput associated with / used to derive statistics ofthe EE / EC / maximum energy credit. The traffic volume / throughput of different entities / resources, i.e. the traffic volume / throughput of one or more PLMN, NF, gNB, RAN node, slice, NF, UE, PDU session, QoS flow etc. i.e. the throughput of UE(s). » Traffic / data / packet rate Maximum / minimum / average / variance traffic rate / packet rate associated with statistics ofthe EE / EC. The traffic / data / packet rate of different entities / resources. » EC ratio (O...max) (NOTE 1) The (list of) ratio / percentage of EC of the entities / resources of the total EC. Maximum is the number of the entities / resources, i.e. PLMN / gNB / RAN node / slice / NF / UE / PDU session / QoS flow, etc., if applicable. The EC ratio / percentage of a network slice (10J) ofthe total EC (100J), the EC ratio / percentage is 10J / 100J = 10%. The ID(s) ofthe entities / resources associated with the ratios are also provided. Using this, a consumer can estimate the percentage ofthe EC of each entity / resource of the total EC. » EC / EE / energy credit rate / band / class (O...max) (NOTE 1) List of groups of entities / resources classified by ranges of EE / EC related parameters. Maximum is the maximum number of classes, i.e. if there are 3 EE / EC classes (low-, medium-, and high-EE / EC classes), the maximum is 3. Using this, a consumer can understand the energy class of each entity / resource and the ratios ofthe entities / resources in each energy class. »> Ratio of entities / resources per energy class The ratio / percentage ofthe entities / resources in the corresponding energy related class. The ratio / percentage ofthe network slices (10) ofthe total number ofthe network slices (100) used to derive the given EE / EC in the high-energy class. In this case, the ratio of entities / resources in the high-energy class is 10 / 100 = 10%. » Validity period The validity period within a time slot for the EC and EE related statistics as defined in clause 6.1.3 of TS28.288. » Spatial validity Area where the EC and EE related statistics apply. » Confidence Confidence ofthe prediction. NOTE 1: Analytics subset that can be used in "list of analytics subsets that are requested", "Preferred level of accuracy per analytics subset" and "Reporting Thresholds". Referring to Figure 1, procedures for a NWDAF to produce NWDAF-based analytics used to assist a communications network to support energy saving and to improve energy efficiency in the network, are shown. 1. The Consumer NF, e.g. AF, PCF or NEF, requests or subscribes to analytics for energy related analytics from NWDAF (possibly via NEF in case the consumer NF is an untrusted AF) and provides input information by invoking either Nnwdaf_AnalyticsSubscription_Subscribe or Nnwdaf_Analyticslnfo_Request. 2a-b. The NWDAF subscribes the service data from an AMF in Table 5, using Namf_EventExposure_Subscribe service for collecting UE location(s) for a UE or a group I list of UEs and other parameters. NOTE: If the NWDAF requires UE location information with finer granularity than TA I cell, then the NWDAF collects the location data from a GMLC instead of the AMF. 2c. The NWDAF subscribes to service data from SMF in Table 5, by invoking Nsmf_EventExposure_Subscribe (Event ID, SUPI(s) or Application ID). In order to provide the requested analytics, the NWDAF subscribes to information of the UE and may subscribe to N4 session related input data from SMFs as defined in Table 5. 2d-e. N4 related input data is provided by UPF to SMF. 2f. SMF provides the requested input data to the NWDAF. 2g-h. The NWDAF may subscribe to input data in Table 1, Table 2, Table 3, Table 4 from the 0AM according to the data collection principles from the 0AM, including data collection from MDAS. 2i-j. The NWDAF may subscribe the service data from AF in Table 5 by invoking Nnef_EventExposure_Subscribe or Naf_EventExposure_Subscribe (Event ID = energy related information, application ID, event filter information, target of event reporting = ID(s) of the entities I resources) service as defined in TS23.502. 2k-l. The NWDAF may subscribe the service data from UPF in Table 5 by invoking Nupf_EventExposure_Subscribe (Event ID = energy related information, application ID, event filter information, target of event reporting = ID(s) of the entities / resources) service as defined in TS23.502. 3. The NWDAF derives requested analytics, in the form of E2E data volume transfer time statistics or predictions or both. 4. The NWDAF provides requested energy related analytics to the consumer, using either Nnwdaf_Analyticslnfo_Request response or Nnwdaf_AnalyticsSubscription_Notify, depending on the service used in step 1. 5-7. If the NF is subscribed to energy related analytics at step 1, when the NWDAF generates new analytics, it notifies the newly generated analytics to the consumer. The NWDAF-based analytics may be new analytics introduced to the 3GPP specifications (i.e. in TS23.288) or may be enhancements of existing analytics (i.e. observed service experience related network data analytics, NF load analytics, etc.) with introducing new energy saving and / or energy efficiency related inputs and outputs. Additional features The following discloses various additional or alternative features that may be provided in certain examples of the present disclosure. The skilled person will appreciate that these features may be combined with any other features disclosed herein. In order to assist with network energy saving and efficiency decision making, determining and enforcing the energy related strategies and policy control, multiple energy related objectives need to be considered by the 5GC and the AF, i.e. the historical, current and future energy consumption and energy efficiency at different granularities (e.g. at RAN level, Core Network level, network slice level, NF level, UE level, PDU session level, and / or QoS flow level). The 5GC and AF may interact and negotiate to determine or update energy saving and energy efficiency decision and energy related strategies and policy control before the service starts and during the service operation. For example, the 5GC and 3rd party may negotiate the maximum energy consumption allowance for providing a service to one or more subscribers by considering the prediction of the feasible energy consumptions and the associated service operation strategy provided by the NWDAF. The NWDAF may also provide the analytics of other service quality information associated to the derived energy related information (e.g. the traffic volume that can be transmitted, the delay and data rate that can be achieved by 5GS using the derived EC or EE) to assist the consumers to make energy saving and efficiency decision making. As a result, the 5CG and AF are able to reach agreements on the implementable energy related policy control that satisfies the requirements of both the AF and 5GC. In order to assist the network energy saving and enhance network energy efficiency, in different use cases, the NWDAF might be required to provide the energy related output analytics at different granularities by the consumer. For example, to make energy saving decision by considering NF (re-)selection or to change the energy states of NFs as required by SA1, the consumer may require the NWDAF to provide the energy related analytics outputs at NF level. To assist with the session management or to determine the implementable energy consumption and efficiency of a service, the consumer may require the NWDAF to provide the energy related analytics outputs at UE level, PDU session level or QoS flow level. Therefore, according to the consumer request, different inputs might be collected by the NWDAF from different data sources, e.g. performance related data collected from OAM, energy monitoring and management related data collected from energy management network function (EMF) if any, QoS monitoring data collected from SMF or UPF, NF load and status data collected from NRF etc. The NWDAF may also collect MDAS-based analytics as the inputs to derive the NWDAF-based analytics. In order to compute the prediction and statistics of the energy consumption and energy efficiency at finer granularities (e.g. at UE level, PDU session level, and / or QoS flow level), the NWDAF can consider the percentage of the total resources of the 5GC, NF, slice or RAN node used by the UE, PDU session or the QoS flow. For example, - The energy consumption of a PDU session can be determined by summing up the percentage of the EC consumed by all network functions that serve this PDU session. - The energy consumption of a QoS flow can be calculated by estimating the percentage of the PDU session or slice resource utilisation by the QoS flow, i.e. the percentage can be estimated by considering the data volume of the specific QoS flow and the overall data volume of the slice the QoS flow belong to. - The energy consumption of a UE can be calculated by summing up the energy consumption of all the PDU sessions or QoS flows that belong to the UE. Based on the energy efficiency (EE) defined in clause 3.1, Energy Efficiency (EE): The relation between a useful output and energy consumption and the definition in clause 6.7 of TS 28.554: the EE of the mobile network could be defined as Data Volume (DV) divided by Energy Consumption (EC) of the considered network elements. The data volume transmitted could be considered as a useful output. Therefore, one way for the NWDAF to determine the EE at different granularity (i.e. slice, UE, PDU session, QoS level) is to use the data volume at the required granularity divided by the Energy Consumption (EC) at the same granularity. Input data In certain examples, the NWDAF may collect the service data from OAM and / or the 5GC network function in charge of energy monitoring and management of the energy consumption and energy efficiency (i.e. EMF, if any). 5 In certain examples, the information / data collected by NWDAF may be one or more items of information / data as defined in Table 10. Table 10 data collected by NWDAF for energy related analytics from OAM and EMF Information Source Description Time window EMF, OAM The time window or the time duration for the collected parameters. The window is between the window start time and stop time. Energy Consumption at 5GC, NF, Network Slice, gNB or NG-RAN level. EMF, OAM Energy consumption (EC) at various granularities, according to output analytics to be derived based on consumer request. The EC can be collected from OAM or EMF (if available). For the Energy Consumption parameters collected from OAM: 5GC Energy Consumption is obtained by summing up the Energy Consumption of all the Network Functions (ECNF) that compose the 5G core network as defined in clause 6.7.3.2 of TS 28.554. NF energy EC is the sum of the energy consumption of PNF(s) and / or VNF(s) which compose the NF, as defined in clause 6.7.3.1 of TS 28.554. Network Slice Energy Consumption is obtained by summing up the Energy Consumption of all the Network Functions (ECNF) that compose the network slice as defined in clause 6.7.3.3 of TS 28.554. The Energy Consumption (EC) of the gNB is obtained by summing up the Energy Consumption of all the Network Functions (NF) that constitute the gNB, as defined in clause 6.7.3.4.2 of TS 23.554. Energy Consumption of the NG-RAN is obtained by summing up the Energy Consumption of all the gNB that constitute the NG-RAN, as defined in clause 6.7.3.4 of TS 28.554. Identifiers (i.e. PLMN ID, NF ID / NF Set ID, S-NSSAI, gNB ID, etc.) EMF, OAM Identifiers of 5GC / NF instance or NF set / slice / RAN node correspond to the collected EC or EE. Energy efficiency of NG-RAN or network slice EMF, OAM The energy efficiency of NR-RAN or network slice as defined in clause 6.7.1 and 6.7.2 of TS 28.554. In certain examples, the NWDAF may also collect data from MDAF. The Type of Energy Efficiency Problem provided by MDAF can help NWDAF with identifying and verifying the derived prediction or statistics of the energy efficiency, i.e. at NF level. In certain examples, the information / data collected by NWDAF may be one or more items of 5 information / data as defined in Table 11. Table 11 input data collected from OAM (MDAF) Information Source Description Timestamp MDAF A time stamp associated with the collected information. Type of Energy Efficiency Problem MDAF Indication of type of the energy efficiency issues. The allowed value is one of the enumerated values: HighEnergyConsumption, LowEenergyEfficiency, Other, Unknown, as defined in 8.4.4 of TS 28.104. In certain examples, in order to determine the EC and EE, in particular the EC and EE at relatively finer granularities (i.e. UE level, PDU session, and QoS flow level), the NWDAF also needs to collect data from other NFs. For example, based on the principle of computing the EC of UE, PDU session or QoS level, the NWDAF needs to determine or estimate the 5 percentage of resources used by the UE, PDU session or QoS level among the overall usage. For example, by acknowledging the number of UEs for a SMF or a slice and the EC of the SMF or slice, the NWDAF is able to determine the average UE level EC. By acknowledging the traffic volume of the UE and that of the associated SMF, and the EC of the SMF, the NWDAF is able to determine the EC of this specific UE. Using similar methods, the NWDAF 10 is able to derive the prediction and statics of the EC and EE at finer granularity by collecting the informative data. In certain examples, the information / data collected by NWDAF may be one or more items of information / data as defined in Table 12. Table 12 data collected by NWDAF for energy related analytics from NFs Information Source Description Time window UPF, SMF, or AF The time window or the time duration for the collected parameters. The window is between the window start time and stop time. UL / DL / overall data volume UPF, SMF, orAF UL / DL / overall (UL and DL) data volume of a slice, a UE, a PDU session, Qos flow, UPF, RAN node, 5GC, according to the consumer request. NF Load information NRF Load per NF number of Qos flows OAM, SMF The number of Qos flows. The measurement can be split into perS-NSSAI at the SMF as defined in clause 5.3.2.1 of TS 28.552. Or the number of the QoS flow per PDU session at the SMF collected from SMF. number of PDU sessions OAM, SMF The number of PDU sessions established by SMF. The measurement can be split into per S-NSSAI as defined in clause 5.3.1 of TS 28.552. Or the number (active) PDU sessions collected from SMF. Number of UEs OAM, AMF The number of UEs register to AMF collected from AMF or the number of subscribers per slice as defined in clause 5.2.1 ofTS 28.552 collected from OAM. In certain examples, in order to derive the output analytics of the renewable energy related information, the NWDAF needs to collect the ratio of renew energy in the total energy supply and the Coefficient of carbon from the 3rd party or EMF if any. In certain examples, the information / data collected by NWDAF may be one or more items of 5 information / data as defined in Table 13. Table 13 Input service data related to renewable energy Information Source Description ratio of renew energy in the total energy supply AF, EMF The ratio of renew energy in the total energy supply Coefficient of carbon AF, EMF Used to derive the carbon emission, i.e. the statistically observed grams per mill watthour for any use of energy in the country or region where the service is provided. Output analytics In certain examples, according to the consumer request, the NWDAF is able to determine the prediction and statistics of energy consumption and energy efficiency along with other corresponding information at different granularities using the input data. 5 In certain examples, the predictions and / or statistics of the NWDAF output analytics may be one or more items of information / data as defined in Table 14. Table 14 prediction and statistics of the output analytics Information Description List of energy-related information^... max) (NOTE 1) Observed statistics and / or predictions during the Analytics target period in the consumer request. The energy-related information includes statistics and / or predictions of maximum, minimum or average value of energy consumption and energy efficiency at the consumer required granularity over the time slot(s). The energy consumption includes the EC of renewable energy. > Time slot entry (1..max) List of time slots within the Analytics target period. The time slots of within which the statistics and / or predictions energy-related information is provided. »Time slot start Time slot start within the Analytics target period. »Duration Duration of the time slot. »Application ID Identifiers of the one or more applications in use during the time slot within which the EE and EC is derived. » Identifiers (i.e. S-NSSAI, NF ID / NF Set ID, gNB ID, PLMN ID, PDU session ID, QFI, UE ID(s) etc.) Identifiers of slice / NF instance or NF set / RAN node / 5GC / PDU session.- / QoS flow / UE that correspond to the EE and EC derived by the NWDAF, based on consumer request. » energy consumption at required granularities The maximum / minimum / average / variance value of EC at the consumer required granularity over a period of time, i.e. at RAN level, Core Network level, network slice level, UE level, PDU session level, and / or QoS flow level. » energy consumption of renewable energy at required granularities The maximum / minimum / average / variance value of renewable energy consumption at the consumer required granularity over a period of time, i.e. at RAN level, Core Network level, network slice level, UE level, PDU session level, and / or QoS flow level. » Carbon emission at required granularities The maximum / minimum / average / variance of Carbon emission at the consumer required granularity over a period of time. » energy efficiency at required granularities The maximum / minimum / average / variance value of energy efficiency at the consumer required granularity over a period of time, i.e. at RAN level, Core Network level, network slice level, UE level, PDU session level, and / or QoS flow level. » maximum energy credit (limit) The upper bound of energy used by the 5G system to provide services provided to one or more subscribers, i.e. the EC of per service per UE level. »traffic volume The traffic volume used to derive the predictions or statistics of the energy related information at the required granularity, i.e. the energy consumption or energy efficiency. »traffic / packet rate The traffic / packet rate associated predictions or statistics of the energy related information at the required granularity or the traffic / packet rate used to derive the EE and EC. »traffic latency / packet delay The traffic latency / packet delay associated predictions or statistics of the energy related information at the required granularity or the traffic / packet rate used to derive the EE and EC. NOTE 1: Analytics subset that can be used in "list of analytics subsets that are requested", "Preferred level of accuracy per analytics subset" and "Reporting Thresholds". Procedures Referring to Figure 2, procedures for NWDAF to derive energy related analytics are shown. 1. The Consumer NF, e.g. AF, PCF or NEF, requests or subscribes to analytics for energy related analytics from NWDAF (possibly via NEF in case the consumer NF is an untrusted AF) and provides the input information as specified in clause 6x2.2 [of 3GPP TR 23.700-66] by invoking either Nnwdaf_AnalyticsSubscription_Subscribe or Nnwdaf_Analyticslnfo_Request. 2a-b. The NWDAF may subscribe to service data from EMF (5GC network function in charge of energy monitoring and management) in Table 6.X.2.2-1 [Table 10 of the present disclosure], if this EMF is available, by invoking Nemf_EventExposure_Subscribe service. 2c. NWDAF subscribes to service data from SMF in Table 6.X.2.1-3 [Table 12 of the present disclosure] by invoking Nsmf_EventExposure_Subscribe (Event ID, SUPI(s) or Application ID). In order to provide the requested analytics, the NWDAF may subscribe to N4 Session related input data from SMFs as defined in Table 6.X.2.1-3 [Table 12 of the present disclosure] 2d. The N4 session event is triggered. 2e-2f. N4 related input data is provided by UPF to NWDAF via SMF. 2f 1. Instead of step 2e-2f, the UPF directly provides the requested N4 related input data to NWDAF. 2g-h. The NWDAF may subscribe to input data in Table 6.X.2.2-1 [Table 10 of the present disclosure] and Table 6.X.2.2-2 [Table 11 of the present disclosure] from the 0AM according to the data collection principles from the 0AM, including data collection from MDAS. 2i-j. The NWDAF may subscribe the service data from AF in the Table 6.X.2.1-3 [Table 12 of the present disclosure] and Table 6.x.2.1-4 [Table 13 of the present disclosure] by invoking Nnef_EventExposure_Subscribe or Naf_EventExposure_Subscribe (Event ID = energy related information, Application ID, Event Filter information, Target of Event Reporting) service as defined in TS 23.502 [3], 2k-I.The NWDAF may subscribe the service data from UPF in the Table 6 x 21-3 [Table 12 of the present disclosure] by invoking Nupf_EventExposure_Subscribe (Event ID = energy related information, Application ID, Event Filter information, Target of Event Reporting) service as defined in TS 23.502 [3], 2m-n. The NWDAF subscribes the service data from AMF in Table 6.X.2.1-3 [Table 12 of the present disclosure] using Namf_EventExposure_Subscribe service. 2o-p. The NWDAF subscribes to the load of NF instances Table 6x21-3 [Table 12 of the present disclosure] by using Nnrf_NFManagement_NFStatusSubscribe. 3. The NWDAF derives requested analytics, in the form of energy consumption and energy efficiency related statistics or predictions or both. 4. The NWDAF provides the requested energy related analytics to the consumer, using either Nnwdaf_Analyticslnfo_Request response or Nnwdaf_AnalyticsSubscription_Notify, depending on the service used in step 1. 5-7. If the consumer NF subscribes to energy related analytics at step 1, when the NWDAF generates new analytics, it notifies the newly generated analytics to the consumer NF. Figure 3 illustrates an exemplary method in a communications network of producing NWDAF-based analytics of energy-related aspects of the network and using the analytics to support energy saving and energy efficiency in the network. This comprises an NWDAF of a communications network collecting energy-related input data from one or more network entities, such as any of a OAM, a AF of the network, 2, the NWDAF using the input data to produce NWDAF-based analytics of energy-related aspects of the network, 4, the NWDAF outputting NWDAF-based analytics of energy-related aspects of the network as any of recommendations, calculations, statistics, predictions of energy-related information, 6. Figure 4 is a block diagram of an exemplary communications network 10 of the disclosure comprising a NWDAF that may be used in certain examples of the present disclosure. The network 10 comprises a NWDAF 12, and a consumer NF 14, a OAM 16, a AF 18, a NRF 20, a SMF 22, a AMF 24, a UPF 26, a MDAF / MDAS 28 and a EMF 30, connected to the NWDAF 12. Certain examples of the present disclosure may operate according to the modified version of 3GPP TR 23.700-66 disclosed in the attached annex to this description. The terms and words used herein are not limited to the bibliographical or standard meanings, but, are merely used to enable a clear and consistent understanding of the examples disclosed herein. Throughout the description and claims, the words “comprise”, “contain” and “include”, and variations thereof, for example “comprising”, “containing” and “including”, means “including but not limited to”, and is not intended to (and does not) exclude other features, elements, components, integers, steps, processes, functions, characteristics, and the like. Throughout the description and claims, the singular form, for example “a”, “an” and “the”, encompasses the plural unless the context otherwise requires. For example, reference to “an object” includes reference to one or more of such objects. Throughout the description and claims, language in the general form of “X for Y” (where Y is some action, process, function, activity or step and X is some means for carrying out that action, process, function, activity or step) encompasses means X adapted, configured or arranged specifically, but not necessarily exclusively, to do Y. Features, elements, components, integers, steps, processes, functions, characteristics, and the like, described in conjunction with a particular aspect, embodiment, example or claim are to be understood to be applicable to any other aspect, embodiment, example or claim disclosed herein unless incompatible therewith. While the invention has been shown and described with reference to certain examples, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention, as defined by the appended claims. Abbreviations / Definitions In the present disclosure, the following abbreviations and definitions may be used. 3GPP 3rd Generation Partnership Project 5G 5th Generation cpp oUL 5G Core 5GS 5G System 5QI 5G QoS Identifier AF Application Function AMF Access and Mobility Management Function API Application Programming Interface CA Carrier Aggregation CN Core Network CPU Central Processing Unit CSI Channel State Information CU Central Unit DL Downlink DNN Data Network Name DRX Discontinuous Reception DTX Discontinuous Transmission DU Distributed Unit DV Data Volume EC Energy Consumption EE Energy Efficiency eMBB enhanced Mobile Broadband eNB E-UTRAN node B EnergyServ Energy Efficiency as a Service ES Energy Saving ETSI European Telecommunications Standards Institute E-UTRAN Evolved UMTS Terrestrial Radio Access Network F1 interface between DU and CU gNB 5G base station GPSI General Public Subscription Identifier ID Identity / ldentifi cation KPI Key Performance Indicator MANO NFV Management and Network Orchestration MDA Management Data Analytics MDAF Management Data Analytics Function MDAS Management Data Analytics Service MIoT Massive Internet of Things MNO Mobile Network Operator MR-DC Multi-RAT Dual Connectivity NF Network Function NFV Network Functions Virtualisation NR New Radio NRF Network Repository Function NWDAF Network Data Analytics Function OAM Operations, Administration and Maintenance PDCP Packet Data Conversion Protocol PCC Policy and Charging Control PCF Policy Control Function PDU Packet Data Unit PLMN Public Land Mobile Network PNF QFI Physical Network Function QoS Flow Identifier QoS Quality of Service RAN Radio Access Network 5 RAT Radio Access Technology Rei Release RF Radio Frequency RRC Radio Resource Control RRM Radio Resource Management 10 SA System Architecture SDU Service Data Unit SMF Session Management Function S-NSSAI Single - Network Slice Selection Assistance Information SSB Single Sideband 15 SUPI Subscription Permanent Identifier TN Transport Network TR Technical Report TS Technical Specification UE User Equipment 20 UL UpLink UMTS Universal Mobile Telecommunications System UP User Plane UPF User Plane Function URLLC Ultra Reliable Low Latency Communications 25 VM Virtual Machine VNF Virtualized Network Function WG Working Group X2 interface between 2 base stations Xn interface between n base stations 30 Annex to the description SA WG2 Meeting #S2-160AHE                                   S2-2400389 22 - 29 January, 2024, Electronic                              (Revision of S2-24xxxx) Source: Samsung Title: Kl#3, New Sol: Support for NWDAF-Based Energy Analytics Document for: Approval Agenda Item: 19.4 Work Item / Release:FS_EnergySys / Rel-19 Abstract of the contribution: This contribution proposes new solution for Kl#3. 1. Discussion As it has been documented in the KI description of Kl#3 in clause 5.3: Whether and how to enhance network analytics for network energy saving and network energy efficiency. In order to support the network energy saving and network energy efficiency, enhancements to NWDAF-based analytics to provide the energy related analytics outputs are needed in Rel-19. 2. Proposal It is proposed to adopt the following changes into TR 23.700-66. *** Start of the change *** 6.0  Mapping of Solutions to Key Issues Editor’s note: This clause describes the snapping between solutions and key issues. Table 6.0-1: Mapping of Solutions to Key Issues Soluti Key Issues 1 2 3X 1 2 X X *** Next change (all new text) *** 6.x Sohilion Support for NWDAF-Based Exergy Analytics skx. l Key Issue etappl&g This solution maps to Kl#3. 6.X.2 FmTCfkmal Desenptiue 6.X.2.1 General Description This solution aims to address the Kl#3 to extend the NWDAF-based analytics with energy related aspects to support network energy saving and enhance network energy efficiency. In order to assist with network energy saving and efficiency decision making, determining and enforcing the energy related strategies and policy control, multiple energy related objectives need to be considered by the 5GC and the AF, i.e. the historical, current and future energy consumption and energy efficiency at different granularities (e.g. at RAN level, Core Network level, network slice level, NF level, UE level, PDU session level, and / or QoS flow level). The 5GC and AF may interact and negotiate to determine or update energy saving and energy efficiency decision and energy related strategies and policy control before the service starts and during the service operation. For example, the 5GC and 3rd party may negotiate the maximum energy consumption allowance for providing a service to one or more subscribers by considering the prediction of the feasible energy consumptions and the associated service operation strategy provided by the NWDAF. The NWDAF may also provide the analytics of other service quality information associated to the derived energy related information (e.g. the traffic volume that can be transmitted, the delay and data rate that can be achieved by 5GS using the derived EC or EE) to assist the consumers to make energy saving and efficiency decision making. As a result, the 5CG and AF are able to reach agreements on the implementable energy related policy control that satisfies the requirements of both the AF and 5GC. In order to assist the network energy saving and enhance network energy efficiency, in different use cases, the NWDAF might be required to provide the energy related output analytics at different granularities by the consumer. For example, to make energy saving decision by considering NF (re-)selection or to change the energy states of NFs as required by SAI, the consumer may require the NWDAF to provide the energy related analytics outputs at NF level. To assist with the session management or to determine the implementable energy consumption and efficiency of a service, the consumer may require the NWDAF to provide the energy related analytics outputs at UE level, PDU session level or QoS flow level. Therefore, according to the consumer request, different inputs might be collected by the NWDAF from different data sources, e.g. performance related data collected from 0AM, energy monitoring and management related data collected from energy management network function (EMF) if any, QoS monitoring data collected from SMF or UPF, NF load and status data collected from NRF etc. The NWDAF may also collect MDAS-based analytics as the inputs to derive the NWDAF-based analytics. In order to compute the prediction and statistics of the energy consumption and energy efficiency at finer granularities (e.g. at UE level, PDU session level, and / or QoS flow level), the NWDAF can consider the percentage of the total resources of the 5GC, NF, slice or RAN node used by the UE, PDU session or the QoS flow. For example, The energy consumption of a PDU session can be determined by summing up the percentage of the EC consumed by all network functions that serve this PDU session. The energy consumption of a QoS flow can be calculated by estimating the percentage of the PDU session or slice resource utilisation by the QoS flow, i.e. the percentage can be estimated by considering the data volume of the specific QoS flow and the overall data volume of the slice the QoS flow belong to. The energy consumption of a UE can be calculated by summing up the energy consumption of all the PDU 5 sessions or QoS flows that belong to the UE. Based on the energy efficiency (EE) defined in clause 3.1, Energy Efficiency (EE): The relation between a useful output and energy consumption and the definition in clause 6.7 of TS 28.554: the EE of the mobile network could be defined as Data Volume (DV) divided by Energy Consumption (EC) of the considered network elements. The data volume transmitted could be considered as a useful output. Therefore, one way for the NWDAF to 10 determine the EE at different granularity (i.e. slice, UE, PDU session, QoS level) is to use the data volume at the required granularity divided by the Energy Consumption (EC) at the same granularity. 6.X.2.2 Input Data of Energy Related Analytics The NWDAF may collect the service data from QAM and / or the 5GC network function in charge of energy 15 monitoring and management of the energy consumption and energy efficiency (i.e. EMF, if any). Table 6.X.2.2-1: Data collected by NWDAF for energy related analytics from OAM and EMF Information Sour Description Time window EMF, OAM The time window or the time duration for parameters. The window is between the time and stop time. Energy Consumption at 5GC, NF, f gNB or NG-RAN level. EMF, OAM Energy consumption (EC) at various according to output analytics to be deriv consumer request. The EC can be collected from OAM or EMF For the Energy Consumption parameters c OAM: 5GC Energy Consumption is obtainec up the Energy Consumption of all Functions (ECNF) that compose t network as defined in clause 6.7.3.2 o NF energy EC is the sum of the energy of PNF(s) and / or VNF(s) which compo defined in clause 6.7.3.1 of TS 28.554 Network Slice Energy Consumption is summing up the Energy Consumpti Network Functions (ECNF) that c network slice as defined in clau TS 28.554. The Energy Consumption (EC) of obtained by summing up the Energy C< all the Network Functions (NF) that gNB, as defined in clause 6.7.3.4.2 of Energy Consumption of the NG-RAN i summing up the Energy Consumption that constitute the NG-RAN, as clause 6.7.3.4 of TS 28.554. Identifiers (i.e. PLMN ID, NF ID / T NSSAI, gNB ID, etc.) EMF, OAM Identifiers of5GC / NF instance or NF set / si correspond to the collected EC or EE. Energy efficiency of NG-RAN or net EMF, OAM The energy efficiency of NR-RAN or net defined in clause 6.7.1 and 6.7.2 of TS 28. The NWDAF may also collect data from MDAF. The Type of Energy Efficiency Problem provided by MDAF can help NWDAF with identifying and verifying the derived prediction or statistics of the energy efficiency, i.e. at NF level. Table 6.X.2.2-2: Input data collected from OAM (MDAF) Information Sour Description Timestamp MDAF A time stamp associated with the collected Type of Energy Efficiency Problem MDAF Indication of type of the energy efficiency is The allowed value is one of the enume HighEnergyConsumption, LowEenergyEffit Unknown, as defined in 8.4.4 of TS 28.104 In order to determine the EC and EE, in particular the EC and EE at relatively finer granularities (i.e. UE level, PDU session, and QoS flow level), the NWDAF also needs to collect data from other NFs. For example, based on the principle of computing the EC of UE, PDU session or QoS level, the NWDAF needs to determine or estimate the percentage of resources used by the UE, PDU session or QoS level among the overall usage. For example, by acknowledging the number of UEs for a SMF or a slice and the EC of the SMF or slice, the NWDAF is able to determine the average UE level EC. By acknowledging the traffic volume of the UE and that of the associated SMF, and the EC of the SMF, the NWDAF is able to determine the EC of this specific UE. Using similar methods, the 5 NWDAF is able to derive the prediction and statics of the EC and EE at finer granularity by collecting the informative data. Table 6.X.2.1-3: Data collected by NWDAF for energy related analytics from NFs Information Sour Description Time window UPF, SMF, or Al The time window or the time duration for parameters. The window is between the time and stop time. UL / DL / overall data volume UPF, SMF, or Al UL / DL / overall (UL and DL) data volume of a PDU session, Qos flow, UPF, RAN according to the consumer request. NF Load information NRF Load per NF number of Qos flows OAM, SMF The number of Qos flows. The measurement can be split into per S-SMF as defined in clause 5.3.2.1 of TS 28. Or the number of the QoS flow per PDU : SMF collected from SMF. number of PDU sessions OAM, SMF The number of PDU sessions established I The measurement can be split into per defined in clause 5.3.1 of TS 28.552. Or the number (active) PDU sessions c SMF. Number of UEs OAM, AMF The number of UEs register to AMF collec or the number of subscribers per slice ; clause 5.2.1 of TS 28.552 collected from 0 In order to derive the output analytics of the renewable energy related information, the NWDAF needs collected the ratio of renew energy in the total energy supply and the Coefficient of carbon from the 3rd party or EMF if any. 10 Table 6.X.2.1-4: Input service data related to renewable energy Information Sour Description ratio of renew energy in the total en AF, EMF The ratio of renew energy in the total enerc Coefficient of carbon AF, EMF Used to derive the carbon emission, i.e. tt observed grams per mill watthour for any in the country or region where the service i 6.X.2.3 Energy Related Output Analytics According to the consumer request, the NWDAF is able to determine the prediction and statistics of energy consumption and energy efficiency along with other corresponding information at different granularities using 15 the input data Table 6.X.2.3-1: prediction and statistics of the output analytics Information Description List of energy-related information(l, (NOTE 1) Observed statistics and / or predictions during the Analytics tar in the consumer request. The energy-related information includes statistics and / or prec maximum, minimum or average value of energy consumption efficiency at the consumer required granularity over the time The energy consumption includes the EC of renewable energy > Time slot entry (1..max) List of time slots within the Analytics target period. The time slots of within which the statistics and / or prediction; related information is provided. »Time slot start Time slot start within the Analytics target period. »Duration Duration of the time slot. »Application ID Identifiers of the one or more applications in use during the ti within which the EE and EC is derived. » Identifiers (i.e. S-NSSAI, NF ID / 1 gNB ID, PLMN ID, PDU session ID, ( etc.) Identifiers of slice / NF instance or NF set / RAN node / 5GC / PDU / QoS flow / UE that correspond to the EE and EC derived by thi based on consumer request. » energy consumption at require! granularities The maximum / minimum / average / variance value of EC at tl consumer required granularity over a period of time, i.e. at R / 5 Core Network level, network slice level, UE level, PDU session and / or QoS flow level. » energy consumption of renewa at required granularities The maximum / minimum / average / variance value of renew; consumption at the consumer required granularity over a peri i.e. at RAN level, Core Network level, network slice level, UE Ie session level, and / or QoS flow level. » Carbon emission at required gr; The maximum / minimum / average / variance of Carbon emis; consumer required granularity over a period of time. » energy efficiency at required gr The maximum / minimum / average / variance value of energy the consumer required granularity over a period of time, i.e. a Core Network level, network slice level, UE level, PDU session and / or QoS flow level. » maximum energy credit (limit) The upper bound of energy used by the 5G system to provide provided to one or more subscribers, i.e. the EC of per service level. »traffic volume The traffic volume used to derive the predictions or statistics c energy related information at the required granularity, i.e. the consumption or energy efficiency. »traffic / packet rate The traffic / packet rate associated predictions or statistics of tl related information at the required granularity or the traffic / p used to derive the EE and EC. »traffic latency / packet delay The traffic latency / packet delay associated predictions or stat energy related information at the required granularity or the traffic / packet rate used to derive the EE and EC. NOTE 1: Analytics subset that can be used in "list of analytics subsets that are requested", "F level of accuracy per analytics subset" and "Reporting Thresholds". Figure 6.X.3-1 Procedures for energy related analytics 5 1. The Consumer NF, e.g. AF, PCF or NEF, requests or subscribes to analytics for energy related analytics from NWDAF (possibly via NEF in case the consumer NF is an untrusted AF) and provides the input information as specified in clause 6.X.2.2 by invoking either NnwdafAnalyticsSubscriptionSubscribe or NnwdafAnalyticsInfoRequest. 2a-b. The NWDAF may subscribe to service data from EMF (5GC network function in charge of energy monitoring and management) in 10 In certain examples, the information / data collected by NWDAF may be one or more items of information / data as defined in Table 10. Table , if this EMF is available, by invoking NemfEventExposureSubscribe service. 2c. NWDAF subscribes to service data from SMF in Table 6.x.2.1-3 by invoking NsmfEventExposureSubscribe (Event ID, SUPI(s) or Application ID). 15 In order to provide the requested analytics, the NWDAF may subscribe to N4 Session related input data from SMFs as defined in Table 6.x.2.1-3 2d. The N4 session event is triggered. 2e-2f. N4 related input data is provided by UPF to NWDAF via SMF. 2f 1. Instead of step 2e-2f, the UPF directly provides the requested N4 related input data to NWDAF. 2g-h. The NWDAF may subscribe to input data in In certain examples, the information / data collected by NWDAF may be one or more items of information / data as defined in Table 10. Table and Table 6.X.2.2-2 from the 0AM according to the data collection principles from the 0AM, including data collection from MD AS. 2i-j. The NWDAF may subscribe the service data from AF in the Table 6.X.2.1-3 and In certain examples, the information / data collected by NWDAF may be one or more items of information / data as defined in Table 13. Table by invoking Nnef_EventExposure_Subscribe or Naf_EventExposure_Subscribe (Event ID = energy related information. Application ID, Event Filter information, Target of Event Reporting) sen ice as defined in TS 23.502 [3]. 2k-l. The NWDAF may subscribe the service data from UPF in the Table 6.x.2.1-3 by invoking Nupf EventExposure Subscribe (Event ID = energy related information, Application ID, Event Filter information, Target of Event Reporting) service as defined in TS 23.502 [3], 2m-n. The NWDAF subscribes the service data from AMF in Table 6.x.2.1-3 using Namf EventExposure Subscribe service. 2o-p.The NWDAF subscribes to the load of NF instances Table 6.X.2.1-3 by using Nnrf_NFManagement_NFStatusSubscribe. 3. The NWDAF derives requested analytics, in the form of energy' consumption and energy efficiency related statistics or predictions or both. 4. The NWDAF provides the requested energy related analytics to the consumer, using either Nnwdaf AnalyticsInfo Request response or NnwdafAnalyticsSubscriptionNotify, depending on the service used in step 1. 5-7. If the consumer NF subscribes to energy related analytics at step 1, when the NWDAF generates new analytics, it notifies the newly generated analytics to the consumer NF. Isunocfe on nxistinn NWDAF: Collect new energy related inputs from 5GC NFs (i.e. SMF, EMF), AF and 0AM. Generate new energy related output analytics including predictions and statistics. Expose energy related analytics outputs to consumers. AF: Expose energy related information to NWDAF. EMF: Expose energy related information at the required granularity to NWDAF. End of the change ***

Claims

1. Ina communications network, a method of producing network data analytics functionbased, NWDAF-based, analytics of energy-related aspects of the network and using the analytics to support energy saving and energy efficiency in the network.

2. A method according to claim 1 in which using the analytics to support energy saving and energy efficiency in the network comprises consumers of the network using the analytics to any of determine, control, switch, modify energy-related network operation strategies, determine energy consumption, EC, and energy efficiency, EE, of a service of the network, determine allowed energy credit, energy consumption of transmitting a traffic volume to one or more user equipment, UE, of the network.

3. A method according to claim 1 or claim 2 in which producing NWDAF-based analytics of energy-related aspects of the network comprises the NWDAF collecting energy-related input data from one or more network entities.

4. A method according to claim 3 in which the network entity comprises an operations, administration and maintenance, CAM, entity and the energy-related input data collected from the 0AM comprises any ofa time window in which data is collected,predictions of traffic load trend of one or more cells,energy saving recommendations for NG-RAN,energy saving recommendations for 5GC,energy saving recommendations for one or more NF of the network,number of QoS flows,resource usage of one or more NF of the network,energy consumption at any of 5GC, NF, Network Slice, gNB or NG-RAN level, identifiers corresponding to the energy consumption,energy efficiency at any of 5GC, NF, Network Slice, gNB or NG-RAN level, identifiers corresponding to the energy efficiency,5GC energy consumption obtained by summing energy consumption of all network functions of the 5GC,NF energy consumption obtained by summing energy consumption of any of one or more PNF of the NF, one or more VNF of the NF,network slice energy consumption obtained by summing energy consumption of one or more network functions of the network slice,gNB energy consumption obtained by summing energy consumption of all network functions of the gNB,NG-RAN energy consumption obtained by summing energy consumption of gNB of the NG-RAN,identifiers of any of 5GC, NF instance, NF set, slice, RAN node,NG-RAN energy efficiency,network slice energy efficiency,number of UEs,ratio of renewable energy in the total energy supply, coefficient of carbon, carbon emission.

5. A method according to claim 3 or claim 4 in which the network entity comprises an application function, AF, and the energy-related input data collected from the AF comprises any of service data, a time window in which data is collected, an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, a downlink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, an uplink and downlink an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC.

6. A method according to any of claims 3 to 5 in which the network entity comprises a network repository function, NRF, and the energy-related input data collected from the NRF comprises any of NF status, NF-related load data, QoS monitoring data, number of UEs of at least a part of the network, QoS flow data, PDU session data.

7. A method according to any of claims 3 to 6 in which the network entity comprises a session management function, SMF, and the energy-related input data collected from the SMF comprises any of QoS monitoring data, a time window in which data is collected, an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, a downlink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, an uplink and downlink an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, number of QoS flows, number of PDU sessions.

8. A method according to any of claims 3 to 7 in which the network entity comprises a user plane function, UPF, and the energy-related input data collected from the UPF comprises any of QoS monitoring data, a time window in which data is collected, an uplink data volumeof any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, a downlink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, an uplink and downlink an uplink data volume of any of a slice, a UE, a PDU session, a QoS flow, a UPF, a RAN node, a 5GC, number of QoS flows, number of PDU sessions.

9. A method according to any of claims 3 to 8 in which the network entity comprises a management data analytics service / function, MDAS / MDAF, and the energy-related input data collected from the MDAS / MDAF comprises any of a timestamp associated with collected input data, EnergyEfficiencyProblematicObject indication of cells or NFs where energy efficiency issues may occur, Energy Efficiency ProblemType indication of type of energy efficiency issues, TrafficLoadTrend predictions in a certain time period, RANEnergySavingRecommendations, CNEnergySavingRecommendations.

10. A method according to any of claims 3 to 9 in which the network entity comprises an access and mobility management, AMF, and the energy-related input data collected from the AMF comprises any of a number of UEs, a maximum number of UEs registered to the AMF, a mean number of UEs registered to the AMF, a number of subscribers per slice of the network.

11. A method according to any of claims 3 to 10 in which the network entity comprises an energy monitoring and management function, EMF, and the energy-related input data collected from the EMF comprises any of energy monitoring and management data,energy consumption and corresponding IDs at any of 5GC, NF, Network Slice, gNB or NG-RAN level,energy efficiency and corresponding IDs at any of 5GC, NF, Network Slice, gNB or NG-RAN level,5GC energy consumption obtained by summing energy consumption of all network functions of the 5GC,NF energy consumption obtained by summing energy consumption of one or more PNF and / or one or more VNF of the NF, network slice energy consumption obtained by summing energy consumption of one or more network functions of the network slice,gNB energy consumption obtained by summing energy consumption of one or more network functions of the gNB,NG-RAN energy consumption obtained by summing energy consumption of gNB of the NG-RAN,identifiers comprising any of a NF ID, a NF set ID, a S-NSSAI, a gNB ID,identifiers of any of 5GC, NF instance, NF set, slice, RAN node,NG-RAN energy efficiency, network slice energy efficiency.

12. A method according to any preceding claim in which the analytics of energy-related aspects of the network produced by the NWDAF comprise one or more recommendations for energy-related information comprising any of reducing network energy consumption, improving network energy efficiency, improving network energy saving.

13. A method according to any preceding claim in which the analytics of energy-related aspects of the network produced by the NWDAF comprise calculations of energy-related information comprising any of network energy consumption information, network renewable energy consumption information, network energy efficiency information, network carbon emission information.

14. A method according to any preceding claim in which the analytics of energy-related aspects of the network produced by the NWDAF comprise statistics of energy-related information comprising any of network energy consumption information, network renewable energy consumption information, network energy efficiency information, network carbon emission information.

15. A method according to any preceding claim in which the analytics of energy-related aspects of the network produced by the NWDAF comprise predictions of energy-related information comprising any of network energy consumption information, network renewable energy consumption information, network energy efficiency information, network carbon emission information.

16. A method according to any preceding claim in which the analytics of energy-related aspects of the network produced by the NWDAF comprise network service quality information associated with the energy-related information comprising any of traffic volume data, traffic rate data, packet delay data.

17. A method according to any preceding claim in which the analytics of energy-related aspects of the network produced by the NWDAF comprise any of one or more specified analytics target periods,one or more time slots within an analytics target period within which the energy-related information is provided,identifiers of one or more specified network granularities,energy-related information provided for one or more specified network granularities and one or more specified analytics target periods,maximum I minimum I average I variance of energy consumption for one or more specified network granularities during an analytics target period,maximum / minimum / average / variance of renewable energy consumption for one or more specified network granularities during an analytics target period,maximum / minimum I average / variance of energy efficiency for one or more specified network granularities during an analytics target period,maximum I minimum / average I variance of carbon emission for one or more specified network granularities during an analytics target period,energy credit for one or more specified network granularities during an analytics target period, an energy credit limit for one or more specified network granularities during an analytics target period.

18. A method according to any preceding claim in which the analytics of energy-related aspects of the network produced by the NWDAF comprise any oftraffic volume used to derive energy-related information for one or more specified network granularities during an analytics target period,traffic / packet rate used to derive energy-related information for one or more specified network granularities during an analytics target period,traffic latency I packet delay associated with energy related information for one or more specified network granularities during an analytics target period, traffic rate I packet rate used to derive energy-related information for one or more specified network granularities during an analytics target period,one or more energy saving recommendation for any of a 5GC, NG-RAN of the network over an analytics target period.

19. A method according to any preceding claim in which the analytics of energy-related aspects of the network produced by the NWDAF comprise any of identifiers of any of one or more network gNB associated with energy consumption, EC, / energy efficiency, EE, one or more network RAN node associated with EC I EE, one or more NF associated with EC / EE, one or more NF instance associated with EC I EE, one or more NF set associated with EC I EE, QoS requirements used to derive EC / EE, QoS characteristics used to derive EC / EE, 5QI of traffic used to derive EC / EE, recommended QoS requirements associated with EE / EC, QoS characteristics associated with EE I EC, 5QI of the traffic associated with EE I EC, PCC rules used to derive EC I EE, control policies used to derive EC / EE, recommended PCC rules associated with EE / EC, control policies associated with EE / EC.

20. A method according to any of claims 17 to 19 in which the one or more specified analytics target periods are specified by a consumer of the network and the one or more specified network granularity are specified by a consumer of the network and comprise any of PLMN level, RAN level, core network level, network slice level, UE level, PDU session level, QoS flow level.

21. A method according to any of claims 17 to 20 in which the NWDAF-based analytics are produced at the one or more specified granularities by considering percentages of total resources of the granularity.

22. A method according to any preceding claim in which NWDAF-based analytics of renewable energy are produced by collecting a ratio of renewable energy in a total energy supply and a coefficient of carbon.

23. A method according to any preceding claim in which the analytics of energy-related aspects of the network are produced by the NWDAF according to request information from a consumer of the network comprising any of one or more analytics IDs,one or more targets of analytics reporting comprising any of an optional entity, an optional resource, a single entity, a single resource, any entity or entities of a group of entities, any resource or resources of a group of resources, one or more network slice levels, a NF instance, a NF set level, a PDU session level, a QoS flow level, a set of QoS flow levels, UE level, a single UE, single SUPI / GPSI UE, a group of UEs, analytics filter information, comprising any of DNN, S-NSSAI, application ID, one or more NF instance IDs, one or more NF set IDs, one or more QoS requirements, one or more QoS characteristics, one or more 5Qls, a list of requested analytics subsets, one or more areas of interest,an analytics target period indicating a time period over which statistics or predictions are requested,a notification correlation ID that is included in a subscription, a notification target address that is included in a subscription, preferred level of accuracy of analytics, one or more reporting thresholds,a list of analytics subsets, preferred granularity of energy-related information comprising any of PDU session level, QoS flow level, application level.

24. A method according to any preceding claim in which a procedure for producing NWDAF-based analytics comprises:

1. a consumer NF, e.g. AF, PCF or NEF, requests or subscribes to analytics from the NWDAF and provides input information by invoking either Nnwdaf_AnalyticsSubscription_Subscribe or Nnwdaf_Analyticslnfo_Request, 2a-2b. the NWDAF subscribes to service data from an EMF by invoking Nemf_EventExposure_Subscribe service, 2c. the NWDAF subscribes to service data from an SMF by invoking Nsmf_EventExposure_Subscribe (Event ID, SUPI(s) or Application ID), 2d. a N4 session event is triggered,2e-2f. N4 related input data is provided by a IIPF to the NWDAF via a SMF,2f 1. Instead of step 2e-2f, a UPF directly provides the requested N4 related input data to the NWDAF,2g-h. the NWDAF subscribes to input data from a 0AM according to the data collection principles from the OAM, including data collection from a MDAS,2i-j. the NWDAF subscribes to service data from a AF by invoking Nnef_EventExposure_Subscribe or Naf_EventExposure_Subscribe (Event ID = energy related information, Application ID, Event Filter information, Target of Event Reporting) service,2k-l. the NWDAF subscribes to service data from a UPF by invoking Nupf_EventExposure_Subscribe (Event ID = energy related information, Application ID, Event Filter information, Target of Event Reporting) service,2m-n. the NWDAF subscribes to service data from a AMF usingNamf_EventExposure_Subscribe service,2o-p. the NWDAF subscribes to load of NF instances by usingNnrf_NFManagement_NFStatusSubscribe,3. the NWDAF derives requested analytics, in the form of energy consumption and energy efficiency related statistics or predictions or both,4. the NWDAF provides the requested energy related analytics to the consumer NF, using either Nnwdaf_Analyticslnfo_Request response or Nnwdaf_AnalyticsSubscription_Notify, depending on the service used in step 1,5-7. if the consumer NF subscribes to energy related analytics at step 1, when the NWDAF produces new analytics, it notifies the newly-produced analytics to the consumer NF.

25. A NWDAF of a communications network which produces analytics of energy-related aspects of the network according to the method of any of claims 1 to 24.Application No: GB2415580.6Claims searched: 1 and 25Examiner: Mr NishanthMuraleedharanDate of search: 15 April 2025Patents Act 1977: Search Report under Section 17Documents considered to be relevant:Category Relevant to claims Identity of document and passage or figure of particular relevance v A 1 and 25 US 2023 / 0269142 Al (ZHAO) See paragraph 0011. X 1 and 25 US 2023 / 0362807 Al (KODAYPAK et al.) See paragraph 0104. X 1 and 25 WO 2023 / 186334 Al (LENOVO SINGAPORE) See paragraph 0044. X 1 and 25 WO 2022 / 058049 Al (ERICSSON TELEFON) See page 10. X 1 and 25 US 2022 / 0408293 Al (HAN et al.) See figure 8 and paragraph 0029. X 1 and 25 EP 4156737 Al (NOKIA TECHNOLOGIES) See paragraph 0037.Categories:X Document indicating lack of novelty or inventive step A Document indicating technological background and / or state of the art. Y Document indicating lack of inventive step if P Document published on or after the declared priority date but combined with one or more other documents of same category. before the filing date of this invention. & Member of the same patent family E Patent document published on or after, but with priority date earlier than, the filing date of this application.Field of Search:International Classification:Subclass Subgroup Valid From H04W 0052 / 02 01 / 01 / 2009 H04L 0041 / 142 01 / 01 / 2022

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