Optimizing power usage using cell disconnection

Abstract

A method is disclosed comprising obtaining historical data from a plurality of access nodes comprised in a network, determining for at least one of the plurality of access nodes a region comprising a set of values for a pair of threshold values, the pair of threshold values comprising a minimum pair of threshold values and a maximum pair of threshold values, determining one pair of threshold values from the region, wherein the pair of threshold values defines a predetermined threshold value for determining whether a cell is to be turned on or off, providing the pair of threshold values to the network for deployment, and collecting data from the plurality of access nodes regarding at least one key performance indicator.

Description

Technical Field

[0001] The following exemplary embodiments relate to energy savings consumed within wireless and cellular communication networks. Background Technology

[0002] Cellular communication networks include the ability to handle communications during peak hours. However, the resources required for peak-hour processing may not always be needed. Therefore, optimizing resource operation time can help reduce the energy required by the network. Summary of the Invention

[0003] The scope of protection sought by the various embodiments of the present invention is defined by the independent claims. Exemplary embodiments and features (if any) described herein that are not within the scope of the independent claims should be interpreted as examples useful for understanding the various embodiments of the invention.

[0004] According to a first aspect, an apparatus is provided, comprising components for: acquiring historical data from a plurality of access nodes included in a network; determining, for at least one of the plurality of access nodes, a region comprising a set of values ​​for threshold pairs, the threshold pairs including a minimum threshold pair and a maximum threshold pair; determining a threshold pair from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; providing the threshold pair to the network for deployment; and collecting data from the plurality of access nodes regarding at least one key performance indicator.

[0005] According to a second aspect, an apparatus is provided comprising at least one processor and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured together with the at least one processor to cause the apparatus to: acquire historical data from a plurality of access nodes included in a network; determine, for at least one of the plurality of access nodes, a region comprising a set of values ​​for threshold pairs, the threshold pairs including a minimum threshold pair and a maximum threshold pair; determine a threshold pair from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; provide the threshold pair to the network for deployment; and collect data from the plurality of access nodes regarding at least one key performance indicator.

[0006] According to a third aspect, a method is provided, the method comprising: acquiring historical data from a plurality of access nodes included in a network; determining, for at least one of the plurality of access nodes, a region comprising a set of values ​​for threshold pairs, the threshold pairs including a minimum threshold pair and a maximum threshold pair; determining a threshold pair from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; providing the threshold pair to the network for deployment; and collecting data from the plurality of access nodes regarding at least one key performance indicator.

[0007] According to a fourth aspect, a computer program product is provided, the computer program product including instructions for causing a device to perform at least the following operations: acquiring historical data from a plurality of access nodes included in a network; determining, for at least one of the plurality of access nodes, a region including a set of values ​​for threshold pairs, the threshold pairs including a minimum threshold pair and a maximum threshold pair; determining a threshold pair from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; providing the threshold pair to the network for deployment; and collecting data from the plurality of access nodes regarding at least one key performance indicator.

[0008] According to a fifth aspect, a computer program is provided, the computer program including instructions for causing an apparatus to perform at least the following operations: acquiring historical data from a plurality of access nodes included in a network; determining, for at least one of the plurality of access nodes, a region comprising a set of values ​​for threshold pairs, the threshold pairs including a minimum threshold pair and a maximum threshold pair; determining a threshold pair from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; providing the threshold pair to the network for deployment; and collecting data from the plurality of access nodes regarding at least one key performance indicator.

[0009] According to a sixth aspect, a non-transitory computer-readable medium is provided, the non-transitory computer-readable medium including program instructions for causing an apparatus to perform at least the following operations: acquiring historical data from a plurality of access nodes included in a network; determining, for at least one of the plurality of access nodes, a region including a set of values ​​for threshold pairs, the threshold pairs including a minimum threshold pair and a maximum threshold pair; determining a threshold pair from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; providing the threshold pair to the network for deployment; and collecting data from the plurality of access nodes regarding at least one key performance indicator.

[0010] According to a seventh aspect, a non-transitory computer-readable medium is provided including program instructions stored thereon, the program instructions being configured to perform at least the following operations: acquiring historical data from a plurality of access nodes included in a network; determining, for at least one of the plurality of access nodes, a region comprising a set of values ​​for threshold pairs, the threshold pairs including a minimum threshold pair and a maximum threshold pair; determining a threshold pair from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; providing the threshold pair to the network for deployment; and collecting data from the plurality of access nodes regarding at least one key performance indicator. Attached Figure Description

[0011] The present invention will now be described in more detail with reference to the embodiments and accompanying drawings, in which...

[0012] Figure 1 An exemplary embodiment of a radio access network is shown.

[0013] Figure 2 An example is shown that tracks business load and applies pre-configured thresholds.

[0014] Figure 3 An exemplary embodiment of the network architecture in which optimization occurs is shown.

[0015] Figure 4 A flowchart for optimizing a threshold is shown according to an exemplary embodiment.

[0016] Figure 5 An exemplary network-level view of a device capable of performing the optimization process described above is shown.

[0017] Figures 6A-6E A graph showing the simulation results is provided.

[0018] Figure 7 An exemplary embodiment of the device is shown. Detailed Implementation

[0019] The following embodiments are exemplary. Although the specification may refer to "an," "one," or "some" embodiments in various places in the text, this does not necessarily mean that each reference refers to the same embodiment(s), or that a particular feature applies only to a single embodiment. Individual features of different embodiments may also be combined to provide other embodiments.

[0020] As used herein, the term "circuit system" refers to all of the following: (a) hardware circuitry implementations only, such as implementations only in analog and / or digital circuitry systems; and (b) combinations of circuitry and software (and / or firmware), such as (if applicable): (i) combinations of (multiple) processors, or (ii) portions of (multiple) processors / software (including (multiple) digital signal processors), software, and (multiple) memories, which work together to cause the device to perform various functions; and (c) circuitry, such as (multiple) microprocessors or portions of (multiple) microprocessors, which require software or firmware to operate, even if such software or firmware is not physically present. This definition of "circuit system" applies to all uses of the term in this application. As another example, as used herein, the term "circuit system" will also cover implementations only of processors (or multiple processors) or portions of processors and their accompanying software and / or firmware. For example, if applicable to a particular element, the term "circuit system" will also cover baseband integrated circuits or application processor integrated circuits for mobile phones, or similar integrated circuits in servers, cellular network devices, or other network devices. The above embodiments of the circuit system can also be considered as embodiments of components providing embodiments for performing the methods or processes described in this document.

[0021] The techniques and methods described herein can be implemented in various ways. For example, these techniques can be implemented in hardware (one or more devices), firmware (one or more devices), software (one or more modules), or a combination thereof. For hardware implementation, the means(s) of the embodiments can be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), graphics processing units (GPUs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. For firmware or software, the implementation can be executed by a module (e.g., process, function, etc.) of at least one chipset that performs the functions described herein. Software code can be stored in memory cells and executed by a processor. Memory cells can be implemented within or outside the processor. In the latter case, the memory cells can be communicatively coupled to the processor in any suitable manner. Furthermore, the components of the systems described herein can be rearranged and / or supplemented by additional components to facilitate the implementation of the various aspects described therewith, and they are not limited to the precise configurations illustrated in the given figures, as will be understood by those skilled in the art.

[0022] The embodiments described herein can be implemented in communication systems such as at least one of the following: Global System for Mobile Communications (GSM) or any other second-generation cellular communication system, Universal Mobile Telecommunications System (UMTS, 3G) based on basic Wideband Code Division Multiple Access (W-CDMA), High-Speed ​​Packet Access (HSPA), Long Term Evolution (LTE), LTE Advanced, systems based on the IEEE 802.11 standard, systems based on the IEEE 802.15 standard, and / or fifth-generation (5G) mobile or cellular communication systems. However, the embodiments are not limited to the systems given as examples, and those skilled in the art can apply the solutions to other communication systems that provide the necessary characteristics.

[0023] Figure 1 An example of a simplified system architecture is depicted, showing some components and functional entities, which are logical units whose implementations may differ from those shown. Figure 1 The connections shown are logical connections; the actual physical connections may differ. It will be clear to those skilled in the art that the system may also include, in addition to... Figure 1 Other functions and structures besides those shown. Figure 1 The example illustrates a portion of an exemplary radio access network.

[0024] Figure 1 Terminal devices 100 and 102 are illustrated, configured to wirelessly connect to an access node (such as an (e / g)NodeB) 104 providing the cell on one or more communication channels within a cell. Access node 104 may also be referred to as a node. The physical link from the terminal device to the (e / g)NodeB is called an uplink or reverse link, while the physical link from the (e / g)NodeB to the terminal device is called a downlink or forward link. It should be understood that the (e / g)NodeB, or its functionality, can be implemented using any entity suitable for this purpose, such as a node, host, server, or access point. It should be noted that although one cell is discussed in this exemplary embodiment, for simplicity of explanation, multiple cells may be provided by one access node in some exemplary embodiments.

[0025] A communication system may include one or more (e / g)NodeBs, in which case the (e / g)NodeBs may also be configured to communicate with each other via wired or wireless links designed for this purpose. These links may be used for signaling purposes. An (e / g)NodeB is a computing device configured to control the radio resources of the communication system to which it is coupled. An (e / g)NodeB may also be referred to as a base station, access point, or any other type of interface device including a relay station capable of operating in a wireless environment. An (e / g)NodeB includes or is coupled to a transceiver. From the transceiver of the (e / g)NodeB, a connection is provided to an antenna element, establishing a bidirectional radio link to the user equipment. The antenna element may include multiple antennas or antenna elements. The (e / g)NodeB is further connected to the core network 110 (CN or Next Generation Core NGC). Depending on the system, the counterpart on the CN side may be a Serving Gateway (S-GW, for routing and forwarding user data packets), a Packet Data Network Gateway (P-GW, for providing connectivity between the terminal equipment (UE) and an external packet data network), or a Mobility Management Entity (MME), etc.

[0026] A terminal (also known as a UE, user equipment, user terminal, user device, etc.) represents a type of device to which resources on the air interface are allocated and assigned, and therefore any features of the terminal equipment described herein can be implemented by a corresponding device (such as a relay node). One example of such a relay node is a Layer 3 relay (self-backhaul relay) toward a base station. Another example of such a relay node is a Layer 2 relay. Such a relay node may comprise a terminal equipment portion and a distributed unit (DU) portion. For example, a CU (centralized unit) can coordinate DU operations via the F1AP interface.

[0027] Terminal devices can refer to portable computing devices, including wireless mobile communication devices with or without a Subscriber Identity Module (SIM) or embedded SIM, eSIM, including but not limited to the following types of devices: mobile stations (mobile phones), smartphones, personal digital assistants (PDAs), handsets, devices using wireless modems (alarm or measuring devices, etc.), portable computers and / or touchscreen computers, tablets, game consoles, laptops, and multimedia devices. It should be understood that user equipment can also be an exclusive or nearly exclusive uplink-only device, an example of which is a camera or camcorder that loads images or video clips onto a network. Terminal devices can also be devices capable of operating in Internet of Things (IoT) networks, in which objects are provided with the ability to transmit data over a network without human-to-human or human-to-computer interaction. Terminal devices can also utilize the cloud. In some applications, terminal devices may include small portable devices with radio components (such as watches, headphones, or glasses), and computation is performed in the cloud. Terminal devices (or in some embodiments, Layer 3 relay nodes) are configured to perform one or more of the functions of user equipment.

[0028] The various techniques described in this paper can also be applied to cyber-physical systems (CPS) (systems that collaboratively control computing elements of physical entities). CPS can realize and utilize a large number of interconnected ICT devices (sensors, actuators, processors, microcontrollers, etc.) embedded in different locations within physical objects. Mobile cyber-physical systems, in which the physical systems discussed have inherent mobility, are a subcategory of cyber-physical systems. Examples of mobile physical systems include mobile robots and electronic devices transported by humans or animals.

[0029] Furthermore, although the device is depicted as a single entity, different units, processors, and / or memory units can be implemented. Figure 1 (Not all of them are shown in the image).

[0030] 5G supports the use of multiple-input multiple-output (MIMO) antennas, more base stations or nodes than LTE (the so-called small cell concept), including macro sites that cooperate with small cells and employ multiple radio technologies, depending on service requirements, use cases, and / or available spectrum. 5G mobile communications support a wide range of use cases and related applications, including video streaming, augmented reality, different data sharing methods, and various forms of machine-type applications (such as massive machine-type communications (mMTC)), including vehicle safety, different sensors, and real-time control. 5G is expected to have multiple radio interfaces: sub-6GHz, cmWave, and mmWave, and can be integrated with existing legacy radio access technologies such as LTE. Integration with LTE can be implemented, at least in the early stages, as a system where macro coverage is provided by LTE and 5G radio interface access is aggregated to LTE from small cells. In other words, 5G is planned to simultaneously support inter-RAT interoperability (such as LTE-5G) and inter-RI interoperability (inter-radio interface interoperability, such as sub-6GHz-cmWave and sub-6GHz-cmWave-mmWave). One of the concepts believed to be used in 5G networks is network slicing, in which multiple independent and dedicated virtual subnets (network instances) can be created within the same infrastructure to run services with different requirements for latency, reliability, throughput, and mobility.

[0031] The current architecture in LTE networks is entirely distributed across radios and entirely centralized in the core network. Low-latency applications and services in 5G may require bringing content closer to the radios, potentially leading to local breakout and multi-access edge computing (MEC). 5G enables analytics and knowledge generation to occur at the data source. This approach requires leveraging resources that may not be continuously connected to the network, such as laptops, smartphones, tablets, and sensors. MEC provides a distributed computing environment for application and service hosting. It also has the ability to store and process content near cellular subscribers to accelerate response times. Edge computing encompasses a wide range of technologies, such as wireless sensor networks, mobile data acquisition, mobile signature analytics, collaborative distributed peer-to-peer self-organizing networks and processing (which can also be categorized as local cloud / fog computing and grid / mesh computing), dew computing, mobile edge computing, cloudlets, distributed data storage and retrieval, autonomous self-healing networks, remote cloud services, augmented and virtual reality, data caching, the Internet of Things (IoT) (massive connectivity and / or latency critical), and critical communications (autonomous vehicles, traffic safety, real-time analytics, time-critical control, healthcare applications).

[0032] The communication system can also communicate with other networks such as the public switched telephone network or the Internet, and / or utilize the services they provide. The communication network may also be able to support the use of cloud services; for example, at least a portion of the core network operation can be performed as a cloud service (this is in...). Figure 1 (Described by “Cloud” 114). The communication system may also include a central control entity that provides facilities for different operators’ networks to cooperate, for example, in spectrum sharing.

[0033] Edge cloud can be introduced into the radio access network (RAN) by leveraging Network Functions Virtualization (NFV) and Software-Defined Networking (SDN). Using an edge cloud means that access node operations are performed, at least partially, in servers, hosts, or nodes that are operationally coupled to a remote radio head or base station, including the radio portion. Node operations can also be distributed across multiple servers, nodes, or hosts. The application of the cloudRAN architecture enables real-time RAN functions to be executed on the RAN side (in the distributed unit DU 104) and non-real-time functions to be executed in a centralized manner (in the centralized unit CU 108).

[0034] It should also be understood that the workload allocation between core network operations and base station operations may differ from, or even not exist at all, in LTE. Other technologies that can be used include, for example, big data and all-IP, which could change how networks are built and managed. 5G (or New Radio) networks are designed to support multiple hierarchical structures, where MEC servers can be placed between the core and base stations or NodeBs (gNBs). It should be understood that MEC can also be applied to 4G networks.

[0035] 5G can also leverage satellite communications to enhance or supplement the coverage of 5G services, for example by providing backhaul or service availability in areas without terrestrial coverage. Possible use cases include providing service continuity for machine-to-machine (M2M) or Internet of Things (IoT) devices or passengers in vehicles, and / or ensuring the service availability of critical communications and / or future rail / maritime / aviation communications. Satellite communications can utilize geostationary orbit (GEO) satellite systems or low Earth orbit (LEO) satellite systems, such as mega-constellations (systems deploying hundreds of (nano) satellites). Satellites 106 included in the constellation can carry a gNB or at least a portion of a gNB that creates a ground cell. Alternatively, satellite 106 can be used to relay signals from one or more cells to Earth. Ground cells can be created by a ground relay node 104 or by a gNB located on the ground or in a satellite, or a portion of the gNB can be on a satellite, such as a DU, and a portion of the gNB can be on the ground, such as a CU. Additionally or alternatively, high-altitude platform station (HAPS) systems can be used. HAPS can be understood as a radio station located at a fixed point on an object at an altitude of 20-50 kilometers relative to the Earth. Alternatively, HAPS can also be mobile relative to the Earth. For example, broadband access could be provided via HAPS using light solar-powered aircraft and airships that operate continuously for months at an altitude of 20-25 kilometers.

[0036] It should be noted that the depicted system is an example of a radio access system, and the system may include multiple (e / g) NodeBs, terminal equipment may access multiple radio cells, and the system may also include other devices, such as physical layer relay nodes or other network elements. At least one (e / g) NodeB may be a home (e / g) NodeB. Furthermore, multiple different types of radio cells and multiple radio cells may be provided within the geographical area of ​​the radio communication system. Radio cells may be macrocells (or umbrella cells), which are large cells typically tens of kilometers in diameter, or smaller cells such as micro, femtocells, or picocells. Figure 1 The (e / g)NodeB can provide any type of these cells. A cellular radio system can be implemented as a multi-layered network comprising several cells. In some exemplary embodiments, in a multi-layered network, one access node provides one or more cells, and therefore multiple (e / g)NodeBs are required to provide such a network structure.

[0037] To meet the needs of improving communication system deployment and performance, the concept of "plug and play" (e / g) NodeBs was introduced. Besides home (e / g) NodeBs (H(e / g)nodeBs), networks capable of using "plug and play" (e / g) NodeBs can also include home node B gateways or HNB-GWs ( Figure 1 (Not shown in the image). HNB gateways (HNB-GWs) that can be installed in carrier networks can aggregate services from a large number of HNBs back to the core network.

[0038] Cellular communication networks consume a significant amount of energy. For example, the energy consumption of a 4G radio access network (RAN) can account for 20-25% of the total cost of ownership (TCO). Furthermore, the energy demands of future cellular communication networks are likely to increase due to increasing cellular density, massive MIMO, and further advancements. Within the RAN, a large portion of the energy consumption is attributed to the access nodes included within it. Access nodes include power amplifiers (PAs) that consume the majority of the energy, and baseband processing and handover also require energy. Therefore, addressing energy consumption issues would be beneficial. One aspect that can address this is monitoring the usage of physical resource blocks (PRBs). For example, PRB utilization can be monitored on a group of cells called a power saving group (PSG). If PRB utilization drops below a pre-configured threshold, one or more cells can be disconnected using a normal shutdown procedure. Then, when PRB utilization rises above another pre-configured threshold, the cells can be reopened. For example, cells can be disconnected at night.

[0039] Figure 2 This illustration shows an example of tracking traffic load and applying pre-configured thresholds to trigger energy saving by disconnecting cells and then reconnecting them when the traffic load increases above the pre-configured thresholds. The illustration is a diagram of traffic load 202 and time 204. Curve 220 shows the tracked traffic load. The pre-configured thresholds are thresholds 232, 234, and 236, where threshold 232 represents the maximum load of the power-saving cell group, threshold 234 represents the minimum load of the power-saving cell group, and threshold 236 represents the minimum load of the last cell opened in the power-saving cell group. Figure 2 It can be seen that when the business load is lower than the pre-configured threshold 232, there will be two power-saving periods 210.

[0040] Therefore, power-saving periods can be viewed as identified opportunity windows (OWs) during which energy savings can be achieved. For example, these periods can occur at night. Therefore, a pair of load thresholds ρ can be defined within the OW. min ρ max And cells within the same power saving group (PSG) can be turned on / off, for example, if the current PRB utilization rate of a given number of consecutive measurements is <ρ.min Such as occurring every 10 seconds, and the predicted load after disconnection being lower than ρ. max If the current PRB utilization rate of a given number of consecutive measurements is greater than ρ, then the cell is disconnected. On the other hand, if the current PRB utilization rate of a given number of consecutive measurements is greater than ρ... max If an event occurs, such as every 10 seconds, the cell is turned on. The order in which cells within the same PSG are turned on / off can be predefined or determined based on any other suitable criteria.

[0041] When a cell disconnects, its power amplifiers are disabled, which has energy-saving benefits. However, this action can also impact the network, such as increasing the PRB utilization of the remaining cells. During cell disconnection, terminal devices connected to that cell are switched to neighboring cells within the same PSG. This may reduce the throughput of all terminal devices because the average throughput perceived by the terminal devices may decrease as the PRB utilization of each cell increases.

[0042] Therefore, when determining pre-configured thresholds, these thresholds should be determined to achieve maximum energy efficiency while avoiding QoS degradation and frequent cell shutdowns of terminal devices. Furthermore, there may be other objectives, such as avoiding excessively frequent cell shutdowns / power-ups, which could jeopardize network stability and lead to excessively frequent handovers. Therefore, for each PSG, it may be necessary to determine a pair of thresholds, i.e., ρ min ρ max This threshold implementation:

[0043]

[0044] stPr(avg UE throughput(ρ)>Y Mbps)>X

[0045] Where R is the set of all allowed thresholds:

[0046]

[0047] The values ​​of X and Y can be configured, for example, Y = 4, X = 0.95. Therefore, it is desirable to have a pre-configured threshold (ρ) for each PSG. min , ρ max It is optimized to maximize energy savings while ensuring minimum user experience throughput for terminal devices.

[0048] To optimize the values ​​that can then be used to determine pre-configured thresholds (also referred to as pre-determined thresholds), a combination of offline and online optimization can be used. Offline optimization can be understood as optimization that may require measurements from the real-time network, but it does not cause any configuration changes to the real-time network and can therefore be performed without interfering with the network and / or affecting key network performance metrics. Online optimization can be understood as optimization that can be accomplished by configuring the real-time network and measurement results, which may interfere with the network and / or affect key network performance metrics. The network can be a cellular communication network including multiple access nodes. As part of offline optimization, the search for ρ can be defined as a finite and safe search area, which can be called a segment. Once the segment is determined, as part of online optimization, the selection of the threshold as a predetermined threshold can be fine-tuned within the determined segment. After the threshold has been determined, constraints regarding the minimum throughput that the terminal devices served by the network will experience can be fine-tuned in online optimization. It should be noted that constraints can already be considered in order to determine the threshold segment in offline optimization. For example, this can be done by integrating the threshold into an energy-saving xApp.

[0049] Figure 3 An exemplary embodiment of a network architecture in which optimization occurs is illustrated. In this exemplary embodiment, there is an orchestration 310 that may include an Open Network Automation Platform (ONAP) and / or an Operation Support System (OSS). The orchestration 310 is then connected to a RIC 320 and a decomposed RAN 330. The RIC 310 can then operate as an xAPP for threshold optimization. The RIC 310 may have interfaces A1 and O1 for connecting to the orchestration 310 and an interface E2 for connecting to the Central Unit Control Plane (CU-CP) of the decomposed RAN 330. The decomposed RAN may also include a Remote Radio Header Terminal (RRM), a Distributed Unit (DU), and a Central Unit User Plane (CU-CP).

[0050] Figure 4 A flowchart for optimizing a threshold is shown according to an exemplary embodiment. In this exemplary embodiment, optimization begins at step 1 (S1), where an over-the-air (OTT) node, such as a RIC, performs historical data collection from several different PSGs. The historical data may include cell-level information collected over a specific time period (such as two weeks). Thus, historical data can be collected from multiple access nodes included in the network. The collected data may include data metrics, such as the average throughput of terminal devices, PRB utilization, carrier frequency, average channel quality indicator (CQI), and / or timestamps of time windows (such as 15-minute time windows) on which the data metrics are averaged.

[0051] Next, in step 2 (S2), the OTT node determines the threshold region for each PSG g, for example, by calculation. The area can then be fine-tuned later, for example, using online exploration. It can be defined as a set of threshold pairs ρ min ρ max Determined threshold region It can be considered a safe area, and can be determined, for example, by searching a one-dimensional line.

[0052] Safe search area It can have the following properties: it is one-dimensional, for example, it can be parameterized by a single real value r∈[0;1] and / or a minimum and maximum threshold ρ. min and ρ max It is a non-increasing variable on r.

[0053] It should be noted that steps 1 and 2 can be performed offline. This offline phase can be performed at specific time intervals, such as after a specific number of weeks, and the time interval can be expressed as T. offline [Week Number]. This allows for the acquisition of information used to determine new safe search areas. The new data. The redoing of these offline steps can be determined by the user, in other words, manually, or after a triggering event is detected. For example, a triggering event could be that causes the online exploration to stop at one of two extremes, such as a low threshold or a high threshold, which means that, most likely, the optimal point is outside the search area (which therefore needs to be recalculated) and / or the CQI histogram changes drastically, for example due to the construction of a new building.

[0054] It should be noted that step 2 can limit the threshold search area to all areas where thresholds are allowed. For example, from

[0055]

[0056] To the safe region that is a subset of R It should be noted that the area can be determined individually for at least one access node, or individually for each of a plurality of access nodes included in the network. In this exemplary embodiment, the security search area... It has the following attributes: It is a one-dimensional line, and it can be parameterized by a single real value r∈[0; 1]; and the minimum and maximum thresholds ρ min and ρ max Both increase, meaning they do not decrease as r increases from 0 to 1. In other words, the safe region can be considered as a subset of all allowed thresholds, and this subset contains sortable elements such that both the minimum and maximum thresholds are non-decreasing.

[0057] It should also be noted that in step 2, the OTT node can retrieve historical data regarding the following aspects for a specific PSG g: CQI distribution, PRB utilization distribution, and / or available carrier frequency. This historical data can then be used as input in a network simulator and applied to different threshold pairs. Where R is the set of all allowed thresholds. In this example, then for a specific PSG g and each pair of thresholds... This produces two different outputs, making It is an estimate of the average number of inactive cells, which is proportional to the energy saved compared to when all cells are always active, and for each pair of thresholds = An estimate of the probability that the average UE throughput (i.e., average user throughput, which can be understood as the average throughput experienced by the terminal device) is higher than a predefined threshold Y [Mbps] (such as Y = 4 Mbps).

[0058] It should also be noted that in step 2, once all allowed thresholds are applied... And estimated for PSG g and then restricted security area The following can be calculated: The line is considered to pass through the origin ρ = (ρ min , ρ max ) = (0, 0) and ρ max >ρ min That is, ρ max =αρ min Where α = atan(φ) and For each line with an inclination angle φ, the safety auxiliary area Defined as a set of thresholds that represent the probability that the throughput is higher than a threshold Y and sufficiently close to the target X. This can be expressed as... Where ∈ can be predefined as an input (e.g., ∈ = 0.02), and it can be used to define the risk sensitivity in terms of throughput (i.e., QoS): a lower ∈ allows for a limited search area, which may result in lower energy savings, but better QoS performance. Therefore, the line tilt angle φ that guarantees the highest potential energy savings can be selected using the following formula. g :

[0059]

[0060] Furthermore, the safe search area of ​​PSG g can be defined relative to the selected angle φ. g Corresponding auxiliary areas:

[0061] Thus, the safe zone Including only security thresholds helps ensure that the throughput of terminal devices is close enough to the target, i.e.:

[0062]

[0063] This also helps ensure that the actual throughput experienced during online exploration is not too low relative to the target X.

[0064] Another advantage is, as mentioned above, throughout the secure area As the threshold increases, the amount of energy saved increases along this direction, while the throughput decreases, i.e., Pr(avg UE throughput(ρ)>Y Mbps) decreases. Therefore, to maximize the energy savings, we search for a threshold that exactly reaches Pr(avg UE throughput(ρ)>Y Mbps=X. That's sufficient; it can't be higher because it would save more energy, and it can't be lower because it would violate QoS constraints. Therefore, step 2 simplifies the problem at hand, and throughput can be considered the sole objective, with energy maximization naturally emerging as a byproduct.

[0065] Next, in Figure 4 In an exemplary embodiment, in step 3 (S3), the threshold can be fine-tuned as part of the online process. This optimization can be implemented within the RIC or, in some exemplary embodiments, within EdenNet, and it selects the next threshold ρ to be deployed. i The aim is to track the time-varying optimal threshold ρ * This threshold is achieved as: Pr(avg UE throughput(ρ) * ()>Y Mbps)=X, for example Y=4, X=0.95.

[0066] One of the advantages of step 2 is that it simplifies the problem. The online threshold search can focus on the average throughput of the terminal device and ensure that the probability of throughput is always higher than the threshold Y (e.g., Y = 4 Mbps) and equal to the target probability X (e.g., X = 95%), that is, Pr(avg UE throughput(ρ)>Y Mbps)=X.

[0067] During the optimization process, several aspects need to be considered: the function f(ρ) = Pr(avg UE throughput(ρ)>Y Mbps) may be unknown, and therefore it is necessary to observe whether the resulting throughput is actually >Y Mbps after deploying the threshold ρ, and estimate f(ρ) accordingly. Furthermore, if the target probability X is high (e.g., >98%), a considerable number of samples (such as at least >10) may be required. 3Only with a small sample size can f(ρ) be accurately estimated using statistical methods such as Wilson / Jeffreys / Clopper-Pearson confidence intervals. For example, if 10 samples are then collected every hour, assuming the counter collection is every T = 15 minutes and approximately 3 cells per PSG, the threshold will be immutable for at least several weeks, which will affect the algorithm's convergence time and its ability to track environmental changes. Therefore, it is beneficial to use a relatively small sample size and a Bayesian-based method to estimate the value of f(ρ). In step 3, the unknown function f can be parameterized. This can be performed only during the first iteration i = 0. fθ(r) can be chosen as a parameterized version of the true f(ρ) function, where

[0068] ·r∈[0;1] for safe regions Parameterization: r = 0 corresponds to the bottom left point, r = 1 is the top right point, and r ∈ (0; 1) lies between these two.

[0069] ·θ characterization f θ The shape. For example, if f θ If it is a linear function, then:

[0070] f θ (r) = a - br, where θ = [a, b]. Note that b > 0 because f θ It is a decreasing function, so the throughput will decrease as the threshold increases.

[0071] Similarly, in step 3, at the first iteration i = 0, there may be prior beliefs about the parameter θ. Offline computation may have already provided an estimate of the unknown function f, which can be called... It can be the closest The value of parameter θ:

[0072]

[0073] Then the prior belief Pr(θ0) can be defined with respect to the parameter θ as follows: The mean vector and the covariance matrix σI (where I is the identity matrix) are normal multivariate distributions, meaning the parameters are initially independent and each has a variance σ. It should be noted that σ can be user-defined, for example, σ = 0.05.

[0074] Step 3 may also include updating the belief about the parameter θ. In iterations i = 1, 2, ..., for parameters that can be parameterized as r... i Specific threshold ρ i The throughput counter can collect data every T [minute]. Call in Indicates the relationship with r iIs the associated mth thpt sample ≥ Y Mbps? i = [y1, ..., y i This can be applied to all observations prior to iteration i. The belief in the parameter θ can then be updated via Bayes' theorem:

[0075]

[0076] Make Pr(θ) i |θ i-1 The unknown thput function f(.) changes over time due to variations in network conditions, according to its transformation law. Pr(θ) i |θ i-1 This can be set as a normally distributed variable with zero mean, a diagonal covariance matrix, and a fixed variance (e.g., 0.01). This choice allows for a rapid response to changes. Therefore, the number of observations is...

[0077]

[0078] Step 3 may also include selecting the next threshold. In iterations i = 1, 2, ..., the threshold r representing the average probability of achieving the goal relative to the current belief can be selected using the following formula. i :

[0079]

[0080] Where the expectation E is the posterior Pr(θ) determined as described above. i |Y i ).

[0081] The above online threshold exploration process has the following advantages: it adapts to constantly changing environments. This is because it provides a good estimate of f(r). i This approach requires a relatively small sample size of throughput (e.g., 40, corresponding to 4 / 5 hours of KPI collection, every T = 15 minutes). The threshold can be updated at a higher frequency than frequency-based methods, such as every 4 / 5 hours instead of every few weeks, allowing for closer tracking of changes in the optimal threshold. Therefore, the optimal threshold can be determined based on changes occurring in the environment. These changes can include, for example, changes in the location of the endpoints being served daily, changes in CQI distribution, and evolution of traffic density. Furthermore, both long-term and short-term throughput for endpoints can be guaranteed. Because of the rapid adaptation to changes, throughput can also be guaranteed in the short term (e.g., one day), thus avoiding sudden performance degradation.

[0082] Next, in Figure 4 In an exemplary embodiment, in step 4 (S4), the OTT node controlling the access node of PSGg transmits the determined threshold ρ. iNow, a predetermined threshold is established. In other words, the determined threshold is provided to the network for deployment by a central entity of the network, such as ES xApp in the RIC, which then controls the access nodes included in the network. Following this, in step 5 (S5), while deploying the threshold, key performance indicators (KPIs), such as average throughput, are collected by the OTT nodes at fixed intervals T (e.g., T = 15 minutes). Average throughput can be determined by, for example, dividing the cell throughput by the number of connected terminal devices, to obtain the throughput of terminal devices that can be referred to as general terminal devices. Therefore, instead of determining the average throughput of a specific terminal device, it is estimated as a general KPI.

[0083] Since the threshold can be updated, in S6, it is determined whether a change has been detected. If so, the optimization proceeds back to step 1. A change can also be understood as a predetermined amount of time that has elapsed, so the optimization needs to be re-executed. If no change has been detected and there is not enough time to re-execute the offline steps of S1 and S2, the optimization process returns to step 3 (S3).

[0084] Figure 5 An exemplary network-level view of an apparatus capable of performing the optimization process described above is shown. In this exemplary embodiment, there are two Power Service Groups (PSGs) (510 and 520). Both PSGs include multiple access nodes, such as gNBs. The access nodes provide historical data 532 to the RIC using, for example, an E2 interface, where energy-saving optimization is performed as xApp 530. xApp 530 then performs an offline portion including steps 1 and 2 in 540, and as output, region 534 is determined and optimization can continue in the online portion, including step 3 in 550. As part of step 4, deployment 536 is then provided to the access nodes using the E2 interface. Using the E2 interface, 538 KPIs are also collected from the access nodes and collected as a KPI dataset 560, which can then be provided as input to step 3 performed in 550.

[0085] Figure 6A This shows the estimated energy savings for each threshold pair ρ in the PSG. The chart. Figure 6B This diagram illustrates the probability that the average throughput of the terminal device exceeds 4 Mbps for each possible threshold pair ρ in the PSG. A graph representing the simulated estimate. The input dataset used to obtain this graph includes PRB utilization, CQI, carrier frequency, and throughput.

[0086] Figure 6C The diagram depicts the area for determining a safe search zone. The chart. First, a threshold set is defined as a line passing through the origin, with an inclination angle of φ. Throughput performance... Get close enough to target X (left side). Then select the optimal tilt angle to achieve maximum energy efficiency (right side).

[0087] Figure 6D and Figure 6E Simulation results for the online phase of the optimization process described above are shown. In these figures, the performance of the online process is compared with four strategies. First, the optimal strategy is used as a baseline. In the optimal strategy, in each iteration, a threshold that guarantees Pr(thpt>4Mbps) = 95% is selected. Then, a random strategy is used, where in each iteration, the threshold in the search region is... First, a threshold pair is randomly and uniformly selected within the search area. Second, a minimum threshold strategy is used, which always selects the lowest threshold (r=0) within the search area to ensure maximum throughput while achieving minimal energy savings. Finally, a maximum threshold strategy is used, which always selects the highest threshold (r=1) within the search area to ensure maximum energy savings while producing the lowest throughput.

[0088] exist Figure 6D In our online process, we manage to track the optimal threshold that evolves over time, and in this case, the long-term average convergence probability to the target is, for example, 95%, for the portion of the throughput sample above the threshold of 4 Mbps. Furthermore, the average energy savings of our online process are close to the optimal value, with a difference of <1%.

[0089] exist Figure 6E Even in short periods such as 0.5 days or 1 day, the throughput performance does not drop too low relative to the target probability (e.g., 95%), consistently highlighting the algorithms used in the optimization and being able to adaptively keep up with changing network conditions.

[0090] Figure 7 The apparatus 700 illustrates an example embodiment that may be an access node or an apparatus included in an access node. The apparatus may, for example, be a circuit system or chipset suitable for an access node to implement the described embodiments. Apparatus 700 may be an electronic device including one or more electronic circuit systems. Apparatus 700 may include a communication control circuitry 710 (such as at least one processor) and at least one memory 720 including computer program code (software) 722, wherein the at least one memory and the computer program code (software) 722 are configured, together with the at least one processor, to cause apparatus 700 to perform any of the example embodiments of the access node described above.

[0091] The memory 720 can be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory, and removable memory. The memory may include a configuration database for storing configuration data. For example, the configuration database may store a current list of neighboring cells, and in some example embodiments, it may store the structure of frames used in detected neighboring cells.

[0092] The device 700 may further include a communication interface 730, which includes hardware and / or software for establishing a communication connection according to one or more communication protocols. The communication interface 730 can provide the device with radio communication capabilities for communication within a cellular communication system. For example, the communication interface can provide a radio interface to a terminal device. The device 700 may also include another interface toward a core network such as a network coordinator device and / or to an access node in the cellular communication system. The device 700 may also include a scheduler 740 configured to allocate resources.

[0093] Although the invention has been described above with reference to examples in conjunction with the accompanying drawings, it is apparent that the invention is not limited thereto, but can be modified in various ways within the scope of the appended claims. Therefore, all words and expressions should be interpreted broadly and are intended to illustrate rather than limit the embodiments. It will be apparent to those skilled in the art that the concepts of the invention can be implemented in various ways as technology advances. Furthermore, it will be understood by those skilled in the art that the described embodiments can, but must not, be combined with other embodiments in various ways.

Claims

1. An apparatus (700) comprising at least one processor (710) and at least one memory (720) including computer program code, wherein the at least one memory (720) and the computer program code are configured together with the at least one processor (710) to cause the apparatus (700) to: Historical data is obtained from multiple access nodes (104) included in the network; For at least one of the plurality of access nodes (104), a region is determined comprising a set of values ​​for threshold pairs, the threshold pairs comprising a minimum threshold pair and a maximum threshold pair, wherein the region is a subset of all allowed thresholds, wherein the subset comprises elements that can be sorted such that neither the minimum threshold nor the maximum threshold decreases; A threshold pair is determined from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; The threshold pair is determined by estimating the value of a function, the function determining the value of the function such that the probability of the average user throughput being higher than a predetermined threshold is equal to a predetermined value, and the value of the function is estimated using a Bayesian method; The threshold pairs are provided to the network for deployment; as well as Data on at least one key performance indicator is collected from the plurality of access nodes (104).

2. The apparatus (700) according to claim 1, wherein the historical data includes one or more of the following: average throughput, physical resource block utilization, carrier frequency, average channel quality indicator, and timestamps of time windows for averaging the data.

3. The apparatus (700) according to any of the preceding claims, wherein the apparatus (700) is further configured to: after a predetermined time period has elapsed and / or based on a triggering event, again obtain the historical data from the plurality of access nodes, and determine the region comprising the set of values ​​for threshold pairs, the threshold pairs comprising a minimum threshold pair and a maximum threshold pair.

4. The apparatus (700) according to any of the preceding claims, wherein the apparatus (700) is configured to: acquire the historical data offline from the plurality of access nodes (104), and determine the region comprising the set of values ​​for threshold pairs, the region being a search region, the threshold pairs comprising a minimum threshold pair and a maximum threshold pair.

5. The apparatus (700) according to any of the preceding claims, wherein the apparatus (700) is further configured to: individually determine the region for each of the plurality of access nodes (104).

6. The apparatus according to any of the preceding claims, wherein the apparatus (700) is included in or connected to a top node.

7. A method comprising: Historical data is obtained from multiple access nodes included in the network; For at least one of the plurality of access nodes (104), a region is determined comprising a set of values ​​for threshold pairs, the threshold pairs comprising a minimum threshold pair and a maximum threshold pair, wherein the region is a subset of all allowed thresholds, wherein the subset comprises elements that can be sorted such that neither the minimum threshold nor the maximum threshold decreases; A threshold pair is determined from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; The threshold pair is determined by estimating the value of a function, the function determining the value of the function such that the probability of the average user throughput being higher than a predetermined threshold is equal to a predetermined value, and the value of the function is estimated using a Bayesian method; The threshold pairs are provided to the network for deployment; as well as Data on at least one key performance indicator is collected from the plurality of access nodes (104).

8. The method according to claim 7, wherein the method further comprises: After a predetermined time period has elapsed and / or based on a triggering event, the historical data is retrieved again from the plurality of access nodes (104), and the region comprising the set of values ​​for threshold pairs, the threshold pairs including a minimum threshold pair and a maximum threshold pair, is determined.

9. The method according to any one of claims 7 to 8, wherein the method further comprises: The historical data is obtained offline from the plurality of access nodes (104), and the region comprising the set of values ​​for threshold pairs is determined, the region being a search region, the threshold pairs comprising a minimum threshold pair and a maximum threshold pair.

10. The method according to any one of claims 7 to 9, wherein the method further comprises: The region is determined individually for each of the plurality of access nodes (104).

11. The method according to any one of claims 7 to 10, wherein the historical data includes one or more of the following: average throughput, physical resource block utilization, carrier frequency, average channel quality indicator, and timestamps of time windows for averaging the data.

12. A non-transitory computer-readable medium comprising program instructions for causing the apparatus (700) to perform at least the following: Historical data is obtained from multiple access nodes (104) included in the network; For at least one of the plurality of access nodes (104), a region is determined comprising a set of values ​​for threshold pairs, the threshold pairs comprising a minimum threshold pair and a maximum threshold pair, wherein the region is a subset of all allowed thresholds, wherein the subset comprises elements that can be sorted such that neither the minimum threshold nor the maximum threshold decreases; A threshold pair is determined from the region, wherein the threshold pair defines a predetermined threshold for determining whether a cell will be turned on or off; The threshold pair is determined by estimating the value of a function, the function determining the value of the function such that the probability of the average user throughput being higher than a predetermined threshold is equal to a predetermined value, and the value of the function is estimated using a Bayesian method; The threshold pairs are provided to the network for deployment; as well as Data on at least one key performance indicator is collected from the plurality of access nodes (104).

13. The non-transitory computer-readable medium of claim 12, wherein the means (700) is further configured to: after a predetermined time period has elapsed and / or based on a triggering event, again acquire the historical data from the plurality of access nodes, and determine the region comprising the set of values ​​for threshold pairs, the threshold pairs comprising a minimum threshold pair and a maximum threshold pair.

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