Improvements in and relating to energy efficiency in a telecommunication network

Abstract

Analytics data is used to selectively control the operation of a directional beam in a base station forming part of a cell in a telecommunication network. Controlling may comprise switching on / off or reducing the power of the beam, reducing the periodicity of a synchronisation signal block (SSB) burst, or activating / deactivating antenna elements. The beam may be produced by a M-MIMO antenna. The analytics data may identify traffic demand of a geographical area that requires a certain level of resource, whereby the controlling directs a beam towards the location to service the resource requirement. The resource demand may be defined in terms of throughput or latency, with the analytics data provided in the form of a throughput map or latency map. Beam-level energy-saving analytics provide energy-saving recommendations at cell-beam level. Throughput-map analytics may provide map-based throughput analytics to help identify where network traffic is mapped geographically; a latency map provides latency characteristics of the traffic demand geographically. Both maps can provide historical trends and future predictions of throughput and latency requirements at any location on the map.

Description

The present invention relates to techniques which may be employed in a telecommunication network to improve energy efficiency. With the diverse services offered by the current 5G networks and even further enhanced services and massive connectivity proposed for future 6G networks, wireless network traffic increases exponentially. To cope with increased traffic, operators invest in deploying increasing number of Radio Access Nodes, RAN, leading to an exponential increase in energy consumption. Due to increased Operating Expenditure, OPEX, cost for energy consumption and environmental concerns, 3GPP has been studying energy efficiency in 5G networks in multiple working groups (e.g. RAN1 - “3GPP TR 38.864, “Study on network energy savings for NR” V18.0.0, Dec, 2022.” , SA2 “3GPP SP-231192, “Feasibility Study on 5GS Enhancement for Energy Efficiency and Energy Saving”, September, 2023.” and SA5 “3GPP SA5 - S5-236790 - Study on Management Data Analytics phase 3, October 2023”). In Release 19 of the 5G standard, SA5 agreed in a study item, referenced above, to enhance Management Data Analytics, MDA, further and identified energy efficiency analytics as a key topic for investigation. Herein, there is disclosed an enhancement to the existing MDA assisted energy saving analytics defined in 3GPP TS 28.104, “Management and orchestration; Management Data Analytics (MDA)” V17.2.0, Dec, 2022, where novel analytics are provided as additional outputs for energy saving analytics. Network energy saving continues to be a focus area for 3GPP where a number of study items include energy efficiency investigations. MDA assisted energy efficiency analytics is identified as a particular focus for investigation. Energy saving is accomplished, among other techniques, by activating the energy saving mode of a network function (NF) (e.g. User Plane Function, UPF) or New Radio, NR, capacity booster cells. To make an energy saving decision, the Management data analytic service, MDAS, consumer must not only determine where the energy efficiency issues occur but also identify the cause of the energy efficiency issues. The MDAS consumer may request a high or a low energy consumption related analytics report from the MDAS producer depending on the traffic scenario In the known art, energy saving decisions are typically based on the load information of the related cells. The MDA processes the network analysis data and the energy saving-related performance measurements like the Packet Data Convergence Protocol, PDCP, data volume of cells to determine the network energy efficiency. The MDAS producer can utilize historical load information data to predict the load variation of cells at a future period. Furthermore, the MDAS consumer may collect energy saving recommendations with the energy saving state from the MDAS producer to be considered in making the energy saving decisions. A set out above, the location area information of the energy efficiency issue is available with a relatively coarse resolution set at a cell level. This is not typically sufficient to provide recommendations for reducing energy consumption or resolving energy efficiency issues at precise locations i.e., at longitudinal and latitudinal level within a particular cell. For example, the MDAS producer maps a geographical area to cells and if the area is not small enough to represent the exact locations, the reported cells may be misleading and energy may be consumed in the cells unnecessarily. Furthermore, the analytics scope is mainly related to load and lacks in other attributes which may assist in enhancing the energy saving decisions. According to the present invention there is provided an apparatus and method as set forth in the appended claims. Other features of the invention will be apparent from the dependent claims, and the description which follows. According to a first aspect of the present invention, there is provided a method of operating a telecommunication network comprising the step of: using analytics data to selectively control the operation of at least one directional beam in a base station, the base station forming part of a cell in the telecommunication network. In an embodiment, the step of selectively controlling the operation of at least one directional beam comprises switching off or reducing the power of the at least one directional beam. In an embodiment, the at least one directional beam is produced by a M-MIMO antenna, forming part of the base station. In an embodiment, the analytics data identifies traffic demand of at least one geographical area, the at least one geographical area requiring a certain level of resource, whereby the selective control directs one or more directional beams towards the at least one geographical area to service the required certain level of resource. In an embodiment, the certain level of resource is defined in terms of throughput or latency. In an embodiment, the analytics data is provided in the form of a throughput map or a latency map. In an embodiment, if the at least one geographical area is located such that it may be served by a plurality of cells, enabling cooperation between the plurality of cells, such that at least one directional beam is provided by each of the plurality of cells. In an embodiment, a periodicity of a Synchronization Signal Block, SSB, burst is reduced. According to a second aspect of the present invention, there is provided a telecommunication network operable to perform the method of the first aspect. ThroughputMap and LatencyMap, as provided by embodiments of the invention enrich the cell switch on / off decisions for improved energy efficiency and support improved energy efficient Synchronization Signal Block, SSB, beam management. Certain key contributions of embodiments of this invention provide enhancements to the existing MDA energy saving analytics: 1. Introducing beam level energy saving analytics where we provide further energy saving recommendations at cell beam level given that the existing MDA energy saving analytics provides energy saving recommendations only at cell level as detailed above. 2. Introducing throughout and latency demand analytics at a finer granularity with the addition of “ThroughputMap” and “LatencyMap” outputs. “ThroughputMap” and “LatencyMap” support better energy saving decisions for cells or cell beams to move to energy saving state. Furthermore, these “ThroughputMap” and “LatencyMap” analytics output may also enrich the analytics for other MDAS consumers MnSs e.g. load balancing functions. A related mapbased analytics “Radio Environment Map” output can be found in MDA coverage problem analysis where the “Radio Environment Map” provides RSRP and SINR values of the selected cluster of cells mapped against the physical geographical information (longitude, latitude, altitude) of the area where the RAN (NG-RAN and E-UTRAN) cells are deployed. Similar to the “Radio Environment Map” providing coverage related properties of a given area, the ThroughputMap and LatencyMap respectively offer insights for the throughput demand and the latency characteristics of the demand in a given area. These analytics provide a finer granularity and have the potential to enable additional energy savings, over and above known techniques. Embodiments of the invention provide an enhancement to the MDA-assisted energy saving analytics to provide a set of novel outputs herein named “ThroughputMap” and LatencyMap”. These analytics provide a “heat-map” of the throughput and latency requirements in a given area at a finer granularity of latitude and longitude-level. As such, the level of granularity provided is smaller than the previously possible cell-level. The lower level of granularity is preferably provided at a beam-level, thereby allowing individual beams to be controlled (on or off or reduced power), so that resources can be targeted in an energy-efficient manner. Each beam that can be powered down represents a tangible energy saving when the vast number of beams across an entire network are considered. Although a few preferred embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes and modifications might be made without departing from the scope of the invention, as defined in the appended claims. For a better understanding of the invention, and to show how embodiments of the same may be carried into effect, reference will now be made, by way of example only, to the accompanying diagrammatic drawings in which: Figure 1 shows maps representing throughput and latency requirements, according to an embodiment of the invention; Figure 2 shows an illustration of SSB beams covering full cell coverage area as known in the prior art; Figure 3 shows an illustration of SSB beams covering a hotpsot area, according to an embodiment of the invention; Figure 4 shows an illustration of SSB Beams when all antenna elements are active, as known in the prior art; Figure 5 shows an illustration of SSB Beams when some of the antenna elements are powered off, according to an embodiment of the invention; Figure 6 shows an example m-MIMO SSB beam radiation pattern with 2 cells, as known in the prior art; Figure 7 shows an Example energy efficient m-MIMO SSB beam radiation pattern, according to an embodiment of the invention; Figure 8 shows an illustration of network deployment with macro and small cells, according to an embodiment of the invention; Figure 9 shows an illustration of a first scenario in a hotspot area in a single layer network according to an embodiment of the invention; and Figure 10 shows an illustration of a second scenario in a hotspot area in a single layer network according to an embodiment of the invention. Figure 1 shows a representation of a total throughput and the amount of users with their latency requirements. The data is represented as a number of geographical maps, coded to represent the respective throughput and latency requirement. ThroughputMap analytics provide a map-based throughput analytics to help identify where network traffic is mapped geographically. LatencyMap provides latency characteristics of the traffic demand geographically. Both maps can provide historical trends and future predictions of throughput and latency requirements at any location on the map (latitude, longitude, altitude). Artificial Intelligence, Al, models can be trained using known techniques to learn throughput and latency trends and predict future behaviour. Deep learning models for time serious forecasting like convolutional and recurrent neural networks (CNNs and RNNs) or long short-term memory (LSTM) models can be designed and trained using historical throughput and latency data to predict future trends at any given location. Operators typically deploy wireless networks with multiple layers, where a coverage layer provides basic coverage across the intended coverage area, and multiple further capacity layers are deployed closer to the areas where there is high traffic demand, typically densely populated areas, city centres shopping centres, main roads etc. These high traffic demand areas are herein known as “hotspot areas”. A coverage cell typically intends to cover all areas including sparse areas with low traffic demand, whereas capacity cells are intended to cover hotspot areas and introduce much needed additional capacity in these areas. The ThroughputMap and LatencyMap analytics help to identify these hotspot areas in the network and help MDAS to make recommendations on which capacity cells can be switched off / on for energy saving based on changing demand in the network over time. For any hotspot area, all capacity cells providing service in the area (e.g. Reference Signal Received Power ,RSRP >threshold) can be analysed and a subset of these cells can be allocated to serve the hotspot area, depending on throughput and latency requirements, coverage footprint and the available capacity from each of these cells. The remaining capacity cells can then be switched off for energy saving. This is a key means by which the maps provided can be used to directly influence the amount of energy consumed by the network and, hence, its overall efficiency. If unused or little used cells can be switched off or into a lower power state, then the energy they would otherwise consume can be saved. Similarly, ThroughputMap can be used for energy efficient Synchronization Signal Block, SSB, beam management. SSB beams within a single cell are designed to cover a full coverage area of a cell. However, once a hotspot area is identified with ThroughputMap analytics, SSB beams covering only the hotspot can be identified and the remaining SSB beams can be switched off for energy saving. SSB beams from multiple cells can be jointly optimized to cover hotspots area too. The following passages in the description relate to one or more specific use cases, which make use of the ThroughputMap and / or the LatencyMap analytics to directly influence the energy efficiency of the associated telecommunication network. Use of ThroughputMap for Energy Efficient SSB beam management Massive - Multiple Input Multiple Output, M-MIMO, is a key technology for 5G and is predicted to play an important role in 6G networks as the frequency spectrum shifts to higher bands. Directional beams are formed using multiple antenna elements and directional beams are used in both common channels for initial access and in RRC_CONNECTED state. Common signals / channels used for User Equipment, UE, initial access are transmitted in synchronization signal block (SSB) which contains the primary synchronization channel (PSS), secondary synchronization channel (SSS) and physical broadcast channel (PBCH) in a block. An SSB burst set contains a number of SSBs which are transmitted several times in the time domain. Each SSB is associated with a beam and is configured to be transmitted at a different direction to cover the intended coverage area of a cell as depicted in Figure 2, which shows multiple beams emanating from an antenna. The multiple beams cover a wide area, including the hotspot, representing an area of increased capacity demand. To reduce energy consumption, SSB beams which are not required, based on traffic demand, can be deactivated. For example, as depicted in Figure 3, if the expected traffic (hotspot area) can be covered by only 3 beams, then the remaining beams can be deactivated. Furthermore, if the expected traffic is low, the periodicity of each SSB burst can be reduced to further increase energy efficiency. Both of these techniques i.e. a reduction in the number of number of SSBs and a periodicity change of SSB bursts are able to significantly reduce energy consumption in the network. A significant amount of energy can be saved by adjusting the number of active antenna elements on a M-MIMO antenna panel based on traffic demand. When antenna elements are reduced, the SSB beam pattern changes, i.e. the beam gets wider and the overall antenna gain is reduced as depicted in Figure 5, which should be contrasted with the situation illustrated in Figure 4, which shows SSB beams when all antenna elements are active. This potentially impacts the coverage of the cell and hence the location of the hotspot traffic needs to be considered to determine whether the cell will cover the hotspot area when the active number of antenna elements change. The aforementioned energy saving techniques depend on the accuracy of where the expected traffic demand comes from geographically so that it can be correlated with SSB beam coverage areas. Cell level traffic information is usually available in the network from cell level Key Performance Indicators, KPIs. However, the SSB beam level traffic demand requires ThroughputMap analytics as set out herein, since the required level of granularity is simply not available in the prior art. ThroughputMap is able to provide the traffic demand geographically and this can be mapped to coverage area for each cell beam with RFEnviromentMap output, known from prior art MDA coverage analytics, thus providing additional energy saving. Moreover, a traffic hotspot area is not typically covered by a single cell but a number of cells and coordination between the cells is desirable to achieve optimal coverage for the traffic hotspot area. An example m-MIMO SSB beam radiation pattern for 2 cells is shown in Figure 6 where most traffic comes from the hotspot area depicted. In this scenario, the hotspot area (current and predicted) can be identified by “ThroughputMap” analytics and SSB beam level coverage footprint can be obtained from RFEnviromentMap output from MDA coverage analytics. SSB beams can then be optimized as shown in Figure 7 where only 1 beam from Cell A with reduced antenna elements and 2 beams from Cell B with full antenna elements are activated to cover the hotspot area. The rest of the beams are switched off leading to significant energy saving. Switching off undesired SSB beams can also eliminate interference between beams e.g. if two beams from different cells cover the same area, one of them can be deactivated which will reduce interference and also energy consumption. ThroughputMap for cell switch-on decision Consider a network deployment scenario where a coverage layer is provided by macro cells and a capacity layer consists of a number of small cells typically deployed closer to expected hotspot areas. A simple version of this scenario with one macro cell and a number of small cells within the coverage area of the macro cell is illustrated in Figure 8. Small cells provide the required capacity to the network when traffic demand is higher, and they are switched off when traffic demand is lower. The Macro cell provides the overall coverage in the whole area and offers required services during times when the traffic demand is low. In low-traffic scenarios (e.g. during the night) it is preferred that all small cells are switched off. The default procedure is that when macro cell load increases, all of the small cells within the macro cell area need to be switched on. However, in an embodiment of the invention, an energy saving agent in the network (e.g. MDAS consumer) identifies and locates the relevant small cells which provide coverage to where majority of the traffic is (i.e. hotspot area) and switches on only those small cells, thereby leading to energy savings. In this case, an inaccurate decision can lead to the incorrect small cells being switched on, i.e. not providing coverage to the hotspot area and consuming additional energy, or all small cells within the macro cell need to be switched on leading to unnecessary higher energy consumption. To mitigate this, the network uses the ThroughputMap analytics to locate current and predicted traffic demand (hotspot area) geographically and uses RFEnviromentMap output from MDA coverage analytics to drive which small cells contribute into the hotspot area, as the active UEs do not measure small cell reference signals (i.e. small cells are switched off). LatencyMap for Cell Switch-on decision For the same scenario illustrated in Figure 8, although the ThroughputMap will enable MDAS consumer to find the right small cell to switch on based on throughput capacity metrics, it does not take other traffic Quality of Service, QoS, metrics into account. Thus, in addition, the LatencyMap provides a key traffic characteristic, i.e. the latency requirements of the traffic demand. LatencyMap enable MDAS consumer to find the relevant small cells to switch on which can provide the required latency. LatencyMap can be correlated with RFEnviromentMap to map which small cells can provide the required traffic with latency and throughput requirements. In the example of a Ultra Reliable Low Latency Communications, URLLC, traffic hotspot area depicted as “URLLC Traffic” in Figure 8, during the high-traffic scenarios, a hotspot is formed and detected / predicted by ThroughputMap analytics. The Latency requirements of the hotspot are detected by the LatencyMap analytics. By utilizing the RFEnviromentMap output from MDA coverage analytics, two candidate small cells are found to serve into the newly formed hotspot area. Then the LatencyMap gives the additional URLLC insights that this is a URLLC hotspot, and therefore recommends that a suitable small cell that can provide low latency should be selected. Thus, among the two candidate small cells (one labelled “high latency" and one labelled “low latency”), only the one which can provide low latency is selected to be switched on. ThroughputMap for cell switch-off decision ThroughputMap analytics is also useful to improve cell switch-off decision in a single layer network as depicted in Figure 9. Depending on where the current / predicted hotspot is, one of the cells can be switched-off. In Figure 9, hotspot area falls in between cell A and B, where cell B can be switched-off and cell A will be able to cover the hotspot area. However in Figure 10, ThroughputMap analytics detects a small hotspot within Cell B, which can only be served by cell B. Therefore, switching off cell B should be avoided in this case. It is possible to understand 2nd, 3rd best serving cells from UE measurement reports of the active UEs. However, ThroughputMap analytics can provide not only the current hotspot area but also the predicted hotspot area in the future, so that an energy saving decision can be made with prediction enriched throughput demand input. This prevents ping-pong energy saving decisions based on measurement report data only. The following passages provide more details, including update sections of the relevant standards specifications. Enabling data for the prior art energy saving analytics is given in Table 8.4.4.1.2-1 in 3GPP TS 28.104. Additional performance metrics are included in this table to enrich UE throughput, latency and location information so that throughput and latency metrics can be processed with UE location to generate ThroughputMap and LatencyMap metrics for more accurate energy saving decisions The table as follows is drawn from Table 8.4.4.1.2-1 in 3GPP TS 28.104, with the additions arising from embodiments of the invention being shown underlined for ease of identification. Data category Description References Performance measurements PNF Power Consumption: power consumed over the measurement Clause 5.1.1.19.2 of TS 28.552. period PNF Energy consumption: energy consumed Clause 5.1.1.19.3 of TS 28.552. SS-RSRP distribution per SSB (beam) of serving NR cell Clause 5.1.1.22.1 ofTS 28.552. SS-RSRP distribution per SSB (beam) of neighbor NR cell Clause 5.1.1.22.1 ofTS 28.552. PDCP Data Volume of NR cells: PDCP data volume delivered in the downlink and uplink Clause 5.1.2.1 and 5.1.3.6 ofTS 28.552 Traffic load variation: PRB utilization rate; RRC connection number; etc. Clause 5.1.1.2 and 5.1.1.4 ofTS 28.552. UE throughput: UE throughput in downlink and uplink Clause 5.1.1.3 ofTS 28.552. Delay related measurements of UPF Clause 5.4 ofTS 28.552. Data volume of UPF Clause 5.4 of TS 28.552. Virtual resource usage of NF: The virtual CPU usage, virtual memory usage, virtual disk usage of virtual network functions Clause 5.7.1 ofTS 28.552. Average e2e UL / DL delay for a Average e2e uplink delay for a network (clause network slice 6.3.1.8.1 in TS 28.554); Average e2e downlink delay for a network slice (clause 6.3.1.8.2 in TS 28.554). Integrated uplink / downlink delay in Integrated downlink delay in RAN (clause 6.3.1.2 RAN in TS 28.554); Integrated uplink delay in RAN (clause 6.3.1.7 in TS 28.554). Round-trip Packet Delay Round-trip packet delay between PSA UPF and NG-RAN (clause 5.4.8 TS 28.552). MDT reports The RSRPs of UE measurements RSRPs of M1 measurements in TS 32.422 and TS 32.423. The RSRQs of UE measurements RSRQs of M1 measurements in TS 32.422 and TS 32.423. The UE location information UE location of M1 measurements in TS 32.422 and TS 32.423. PDCP Data Volume per UE PDCP SDU Data volume measurement separately for DL and UL, per DRB per UE by gNB of M4 measurements in TS 32.422 and TS 32.423. Average UE throughput measurement Average UE throughput measurement separately for DL and UL, per DRB per UE and per UE for the DL, per DRB per UE and per UE for the UL, by gNB of M5 measurements in TS 32.422 and TS 32.423 Packet Delay measurement Packet delay measurement, separately for DL and UL. per DRB per UE bv gNB of M6 measurements in TS 32.422 and TS 32.423 QoE Data The measurements that are collected are DASH and MTSI measurements TS 28.406. Configuration data MOIs of the cells, UPFs and SMFs TS 28.541. Network analytics data The control plane analysis result from the NWDAF, e.g. observed service experience related network data analytics TS 23.288. UE location UE location information provided by The UE location information provided by LMF via reports the LMF services. service-based interface (see TS 23.273). - The existing analytics output for energy saving analytics is given below in Table 8.4.4.1.3-1, taken from 3GPP TS 28.104. The table is modified to introduce ThroughputMap and 5 LatencyMap, which are added to the analytics output. These new analytics output are shown underlined for ease of identification. Information element Definition Support qualifier Properties energyEfficiencyProblematicObject Indication of NR cells or NFs where the energy efficiency issues occurred or potentially occur. M type: DN multiplicity: 1..* isOrdered: False isUnique: True defaultvalue: None isNullable: False energyEfficiencyProblemType Indication of type of the energy efficiency issues. The allowed value is one of the enumerated values: HighEnergyConsumption, LowEenergy Efficiency, Other, Unknown. M type: enumeration multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: False trafficLoadTrends 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). M ty pe:T rafficLoadT rend multiplicity: 1..* isOrdered: False isUnique: True defaultvalue: None isNullable: False rANenergySavingRecommendation s For ES on NR cells. It may contain a set of: Recommended NR Cell (ES-Cell) to enter energySaving 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 exist only in case of RAN energy saving is supported. CM type: EsRecommendationO nNRcell multiplicity: 1..* isOrdered: False isUnique: True defaultvalue: None isNullable: False rANenerqvSavinqRecommendation Beam For ES on NR cell beams. It may contain a set of: Recommended type: EsRecommendationO nNRcellBeam multiplicity: 1..* NRCell Beam to enter isOrdered: False enerqySavinq state. isUnigue: True Reason for defaultvalue: None enerqySavinqState isNullable: False suggestion Recommended candidate cell beams 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 NR Cell Beam. cNenergySavingRecommendations For ES on UPFs. 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 exist only in case of CN energy saving is supported.. CM type: EsRecommendationO nUPF multiplicity: 1..* isOrdered: False isUnique: True defaultvalue: None isNullable: False statisticsOfCellsEsState The statistic result of current energy saving state of the cells at a certain time, which can be used by consumers to make analysis (e.g. observed service experience analysis made by NWDAF) or to make decision (e.g. enter / exit the energy saving state based on the current energy saving state). O type: StatisticOfCellEsState multiplicity: 1..* isOrdered: False isllnique: True defaultvalue: None isNullable: False Throuqhput Map The graphical description O type: List of the total throuqhput at multiplicity: * each location (lonqitude, isOrdered: False latitude, altitude) for a isUnique: True given area scope. defaultvalue: None It is a list of paired tuples of geographical information (lonqitude, latitude, altitude) and total throuqhput values . isNullable: False Latency Map The qraphical description O type: List of # of users with multiplicity: * low / medium / hiqh latency isOrdered: False at each location isUnique: True (lonqitude, latitude, defaultvalue: None altitude) for a given area isNullable: False scope. It is a list of paired tuples of geographical information (lonqitude, latitude, altitude) and # of users with low / medium / hiqh latency. The new output of energy saving analytics, according to an embodiment, are: ThroughputMap: This output may provide the total throughout in a given location (longitude, 5 latitude, altitude) from all UEs at the same location at the same time. LatencyMap: This output may provide the total number of users requiring a certain level of latency. One solution can group latency requirements into low / medium / high categories and report number of users for each latency category for a given location (longitude, latitude, 10 altitude) and time. Beam Level Energy Saving Recommendations: As detailed above, SSB beams can be switched off for energy saving reasons and energy saving analytics can propose not only cell level but also beam level energy saving recommendations i.e. certain beams at any cell can be 15 recommended to move to an energy saving state (including an off state). The recommendation format can follow the same format from the cell level recommendation. A new data type “EsRecommendationsOnNRCellBeam” and beam level recommendations are reported within this data type. 20 The associated Information elements, IE, of the new “EsRecommendationsOnNRCellBeam” are detailed in the table below: Name Definition Support qualifier Properties esNRCellBeam It provides the DN of Beam which is recommended to enter energySaving state. This would imply that this beam will be switched off during the recommended time window M type: DN multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: False ReasonEnergyS avingState It provides the reason for the recommendation of the beam to enter into energySaving state. Reason can have 2 values: 1- Low traffic 2- Overlapping beam M type: DN multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: False candidateNRcell Beam It provides the DN of candidate Beams which are recommended with precedence for taking M type: DN multiplicity: * over the traffic of ES-Beams. isOrdered: True isllnique: True defaultvalue: None isNullable: False trafficThresholds When the reasonToSwitchOff is “Low Traffic” then, “trafficThresholds” provides the recommended traffic threshold information for the NRCellBeam. The NRCellBeam can enter into energySaving state when the expected load from the beam coverage area is below the threshold value. When the reasonEnergySavingState is “Overlapping beam” then, “trafficThresholds” provides the recommended traffic threshold information for the CandidateNRCell. The NRCellBeam can enter into energySaving state when the expected load from the candidateNRCell is below the threshold value. M type: Thresholdinfo multiplicity: * isOrdered: False isllnique: False defaultvalue: None isNullable: False enterTime It provides the recommended time to enter the energy saving state for the ES-Cell Beam. M type: DateTime multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: False endTime It provides the recommended time to terminate the energy saving state for the ES-Cell Beam. M type: DateTime multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: False At least some of the example embodiments described herein may be constructed, partially or wholly, using dedicated special-purpose hardware. Terms such as ‘component’, ‘module’ or 5 ‘unit’ used herein may include, but are not limited to, a hardware device, such as circuitry in the form of discrete or integrated components, a Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuit (ASIC), which performs certain tasks or provides the associated functionality. In some embodiments, the described elements may be configured to reside on a tangible, persistent, addressable storage medium and may be configured to execute on one or more processors. These functional elements may in some embodiments include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Although the example embodiments have been described with reference to the components, modules and units discussed herein, such functional elements may be combined into fewer elements or separated into additional elements. Various combinations of optional features have been described herein, and it will be appreciated that described features may be combined in any suitable combination. In particular, the features of any one example embodiment may be combined with features of any other embodiment, as appropriate, except where such combinations are mutually exclusive. Throughout this specification, the term “comprising” or “comprises” means including the component(s) specified but not to the exclusion of the presence of others. Attention is directed to all papers and documents which are filed concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features. The invention is not restricted to the details of the foregoing embodiment(s). The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

Claims

1. A method of operating a telecommunication network comprising the step of:using analytics data to selectively control the operation of at least one directional beam in a base station, the base station forming part of a cell in the telecommunication network.

2. The method of claim 1 wherein the step of selectively controlling the operation of at least one directional beam comprises switching off or reducing the power of the at least one directional beam.

3. The method of claim 1 or 2 wherein the at least one directional beam is produced by a M-MIMO antenna, forming part of the base station.

4. The method of any preceding claim wherein the analytics data identifies traffic demand of at least one geographical area, the at least one geographical area requiring a certain level of resource, whereby the selective control directs one or more directional beams towards the at least one geographical area to service the required certain level of resource.

5. The method of claim 4 wherein the certain level of resource is defined in terms of throughput or latency.

6. The method of claim 5 wherein the analytics data is provided in the form of a throughput map or a latency map.

7. The method of any of claims 4 to 6 wherein if the at least one geographical area is located such that it may be served by a plurality of cells, enabling cooperation between the plurality of cells, such that at least one directional beam is provided by each of the plurality of cells.

8. The method of any preceding claim wherein a periodicity of a Synchronization Signal Block, SSB, burst is reduced.

9. A telecommunication network operable to perform the method of any preceding claim.

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