Network Energy Savings in Multi-Radio Access Technology Networks

A method can comprise receiving, by a system, a service request from a user equipment. The method can further comprise, based on the service request, determining respective signal strengths of respective radio access technologies of multiple radio access technologies being used for cellular broadband communications. The method can further comprise, identifying a subset of the respective radio access technologies for which a signal strength criterion is satisfied. The method can further comprise processing a subset of the respective signal strengths corresponding to the subset of the respective radio access technologies using a machine learning model to determine a selected radio access technology, wherein the machine learning model is trained to determine the selected radio access technology based on an energy efficiency metric of the system and based on resource allocation of the system. The method can further comprise communicating with the user equipment via the selected radio access technology.

BACKGROUND

A base station can communicate with user equipment (UE) to facilitate mobile communications, or cellular network communications. In doing so, the base station can consume energy.

SUMMARY

An example method can comprise receiving, by a system, a service request from a user equipment. The method can further comprise, based on the service request, determining, by the system, respective signal strengths of respective radio access technologies of multiple radio access technologies being used for cellular broadband communications. The method can further comprise, identifying, by the system, a subset of the respective radio access technologies for which a signal strength criterion is satisfied. The method can further comprise processing, by the system, a subset of the respective signal strengths corresponding to the subset of the respective radio access technologies using a machine learning model to determine a selected radio access technology of the multiple radio access technologies, wherein the machine learning model is trained to determine the selected radio access technology based on an energy efficiency metric of the system and based on resource allocation of the system. The method can further comprise communicating, by the system, with the user equipment via the selected radio access technology.

An example system can operate as follows. The system can receive a service request from a user equipment, wherein the service request corresponds to facilitating cellular broadband communications. The system can, based on the service request, determine respective signal strengths of respective radio access technologies of a group of multiple radio access technologies usable for the cellular broadband communications. The system can identify a subset of the respective radio access technologies for which corresponding signal strengths satisfy a signal strength criterion. The system can process the corresponding signal strengths of the subset of the respective radio access technologies with a trained machine learning model to determine a selected radio access technology of the group of multiple radio access technologies, wherein the trained machine learning model is configured to determine the selected radio access technology based on an energy efficiency metric of the system and based on resource allocation of the system. The system can communicate with the user equipment using the selected radio access technology.

An example non-transitory computer-readable medium can comprise instructions that, in response to execution, cause a system comprising a processor to perform operations. These operations can comprise, based on receiving a service request from a user equipment, determining respective signal strengths of respective radio access technologies of a group of multiple radio access technologies. These operations can further comprise identifying a subset of the respective radio access technologies for which a corresponding subset of the respective signal strengths satisfies a signal strength criterion. These operations can further comprise processing the respective signal strengths of the subset of the respective radio access technologies with an artificial intelligence model to determine a selected radio access technology of the group of multiple radio access technologies, wherein the artificial intelligence model is trained to determine the selected radio access technology based on an energy efficiency metric of the system and based on resource allocation of the system. These operations can further comprise communicating with the user equipment using the selected radio access technology.

DETAILED DESCRIPTION

Overview

While the examples described herein generally describe specific types of mobile networks, it can be appreciated that the present techniques can be applied to other types of networks. Where optimal results, or optimization techniques, are described, it can be appreciated that these can more generally apply to improvements in examples, such as improvements that satisfy an optimization criterion. A similar approach can be applied to other examples that mention superlatives, such as maximum or least.

Energy consumption of mobile networks can be an important issue for current and next generation networks. In some areas, there has been increasing focus on the use of energy efficient methods, in both the design and operation of mobile networks. The third generation partnership project (3GPP) standards play a key role in defining the requirements and features of multiple generations of mobile networks in conjunction with bodies such as the international telecommunications union (ITU) that provides the objectives for each generation prior to the deliberations on the design of various layers within the standards sub-groups of 3GPP. While specifications from each release can have maintained some notion of backwards compatibility since moving to an orthogonal frequency division multiplexing (OFDM)-based transmission for the 3GPP long term evolution (LTE) specification for the fourth generation (4G), generations prior to it have used different waveforms and core network architecture. Therefore, second generation (2G) and third generation (3G) networks coexisting within the same geographical area can involve a use of different frequency bands using different network equipment due to fundamental differences between the protocol stacks of the various generations. Currently, with the advent of 5G NR, there are proposed features geared towards energy efficiency that can be applicable to what is known as the standalone (SA) implementation. Nonetheless, a practical reality can continue to be the fact that networks from multiple generations continue to coexist, and while there can be plans to accelerate shutting down some of the previous generation mobile networks to free up valuable spectrum for use, the process can be gradual. Therefore, it can be a while before older generation networks give way to more efficient newer generation technology. Furthermore, future generations of mobile networks such as the sixth generation (6G) may also need to consider the coexistence with both 4G and 5G networks going forward. Consequently, approaches to applying energy-saving features across the several generations of mobile networks simultaneously can be a valuable feature that mobile network operators (MNOs) look for.

Mobile networks have evolved over several generations during the course of their usage and is currently onto a fifth generation, or 5G. Over these multiple generations, transmission speeds increased manifold from the second generation mobile networks based on a global system for mobile communications (GSM) standard, to third generation networks based on code division multiple access (CDMA) or wideband-CDMA (W-CDMA), giving rise to enhanced data rates for GSM evolution (EDGE) and high speed packet access (HSPA) standards.

Subsequently, OFDM based networks became a mainstay of mobile network air interface starting from 4G networks to 5G NR, with a host of advanced features that facilitate high-speed low latency data transfers. Traditionally, newer radio access network (RAN) infrastructures have been deployed alongside existing ones, with each new cellular technology roll-out. This deployment strategy can allow for a gradual deployment of new technologies, spreading capital expenditure (CAPEX) over time, and extend a return on investment (ROI) for older network infrastructure. Nonetheless, with the increased capacity, it can be that energy consumption of the networks has also increased exponentially, and thus poses a heavy burden on network operational expenditures (OPEX) along with detrimental effects that the energy consumption has on the environment to produce such large amounts of energy. A transition from one generation to another is generally not universal, and often times a current generation of network equipment can coexist with at least one generation prior to that. In fact, an overall power consumption can rise significantly in deploying a new generation, as more energy is necessary to keep more extensive cellular networks operating.

In some examples, for consolidation and seamless integration, existing 2G and 3G base stations have been replaced with newer infrastructure that run 2G, 3G, and 4G from single radio hardware, and this can be referred to as Multi-Radio Access Technology (M-RAT). M-RAT can generally comprise a co-location of multiple cellular standards into single radio items. In some examples of an M-RAT architecture, communication technology can be any of the RATs such as GSM, W-CDMA, LTE, 5G NR, and Wi-Fi as well. With the advent of 5G and with particular attention to dual connectivity, as outlined in the 3GPP 5G specification, an M-RAT architecture can receive increased attention. Concurrent connectivity to multiple cellular RATs can present a set of issues, such as joint M-RAT assignments, and dynamic power allocation, which has been addressed in some prior approaches statistically by observing mobile equipment behavior with respect to accessing multiple RATs, or a single one.

In some configurations, each RAT in each sector utilizes its own PA and, hence, a multi-RAT site can contain a large number of PAs. When a cell site uses multi-input multi-output (MIMO), and, possibly, multiple frequency bands, a number of PAs can quickly become quite large. Given that PAs can be the most power consuming components in a macro base station, and the fact that they can consume a considerable amount of energy even when carrying no traffic, it can be seen that multi-RAT deployments can lead to an increased concern regarding total energy consumption. Total energy consumption can be a primary driver of equipment footprint at the cell site, and can constitute a large fraction of operators' OPEX. The reported energy consumption of radios can vary depending on numerous factors, such as source, technology, cell size, components, manufacturers, configuration, and radiating power. In general, it can be that the older the cellular technology, the more the energy required to power a single radio. For example, a single 2G GSM base station can require an average power of 3.8 kilowatts (KW) for an annual energy amount of around 33.3 megawatt hours (MWh). A single 3G base station can instead require 1 KW, for an annual consumption of 9 MWh. Values for an LTE base station can be around 0.5 KW, resulting in an annual consumption of 4.5 MWh. However, it can be that this does not take into account reducing cell coverage of successive generations as densification can be a primary way to enhance network capacity. While an LTE base station can have a spectral efficiency that is 30-40 times that of a 2G base station, to offer the same coverage of the latter, an LTE network can consume even more energy.

Furthermore, one or more RATs can also be able to share a PA within multi-RAT radio base stations, in which the radio units can support more than one RAT simultaneously. For such multi-RAT configurations, the radio unit and the power amplifier can be shared between the RATs. The energy consumption in such configurations can be explored using system level simulations with different traffic and transmission environments using certain standardized energy consumption evaluation frameworks. For modern transceiver designs, however, such models can be outdated due to significant advancements in both PA design and state-of-art radio circuitry.

In 5G NR, a rigorous effort can have been made to ensure a higher level of energy efficiency which, even compared to 4G-LTE base stations, can be significantly more than previous generations. For example, for discontinuous transmission (DTX), a concept that was introduced for 4G networks, longer durations and higher ratio of discontinuous transmission (DTX) can be supported in NR compared to a 3GPP LTE standard. Additionally, higher transmission rates of 5G NR can additionally result in even more DTX opportunities, as packet transmission times decrease. These features can allow for implementation of new deep-sleep states for RAN hardware such that the base station can lower its total energy usage significantly. However, a use of certain advanced features of NR can also imply that the required number of base stations for coverage of a similar sized area can potentially be greater than 4G-LTE base stations, and far greater than 2G and 3G. It can be that 2G signals can provide far greater coverage, and thus can be a network of choice for internet of things (IoT) and telematics applications. While it can be that benefits of 5G networks cannot be retrofitted to older networks due to lack of backwards compatibility beyond 4G, a multi-RAT deployment can still be optimized to provide benefits of disparate technologies to users by optimal network selection, and providing a data-driven approach to operational policy optimization. The present techniques can be implemented to increase overall energy efficiency of the network.

The present techniques can be implemented to improve NES for multi-RAT mobile networks by using ML based decision making. The present techniques can also be implemented to facilitate deciding on which mode to operate each RAT in when selected to service a certain set of requests, so as to not violate service level agreements using a network-centric approach. The present techniques can be implemented to provide an online learning approach to incorporate feedback received from in-field operations to improve upon energy savings. The present techniques can also be implemented to provide network operators with a framework that can be used for an integrated approach to network resource management for multi-constraint operation without affecting coverage. The present techniques can also be implemented to facilitate an optimal association of UEs with the base station/RAT based on their preferences (requested QoS, data rate, and contract) for efficient utilization of the available radio resources with an energy efficiency optimization constraint.

Example Architectures

FIG.1illustrates an example system architecture100that can facilitate network energy savings in multi-RAT networks in base stations in accordance with an embodiment of this disclosure.

System architecture100comprises base station102, and user equipment104. In turn, base station102comprises RATs106, and network energy savings in multi-RAT networks component108.

Each of base station102and/or user equipment104can be implemented with part(s) of wireless communications operating environment1000ofFIG.10. Base station102can generally comprise one or more antennas and electronic communications equipment to facilitate network communications with user equipment104. User equipment104can generally comprise a computing device used by an end user to communicate with base station102.

As part of communicating with user equipment104(including examples of communicating with multiple user equipment devices), base station102can determine a RAT with which to communicate with the UE, where the UE is configured to communicate via multiple RATs of RATs106. Determining which RAT to use can be performed to minimize energy consumption in base station102, while also satisfying quality-of-service (QOS) metrics with regard to user equipment104, and also with regard to other user equipment that is communicating with base station102.

In some examples, network energy savings in multi-RAT networks component108can implement part(s) of the process flows ofFIGS.6-9to implement network energy savings in multi-RAT networks in base stations.

It can be appreciated that system architecture100is one example system architecture for network energy savings in multi-RAT networks, and that there can be other system architectures for network energy savings in multi-RAT networks.

FIG.2illustrates an example system architecture200for an integrated multi-RAT architecture that considers a colocation based on functionality, and that can facilitate network energy savings in multi-RAT networks, in accordance with an embodiment of this disclosure. In some examples, part(s) of system architecture200can be used to implement part(s) of system architecture100ofFIG.1.

System architecture200comprises radio unit (RU)202, distributed unit (DU)204, centralized unit (CU)206, power amplifier (PA) and other radio frequency (RF) components208, PA and other RF components210, PA and other RF components212, and PA and other RF components214—each of which may correspond to one of the RATs implemented for the BS. System architecture200further comprises power supply216, physical (PHY) layer218, second generation (2G) PHY220, 3G PHY222, 4G PHY224, fifth generation (5G) PHY226, medium access control (MAC) layer228, 2G traffic scheduler230, 3G traffic scheduler232, 4G traffic scheduler234, 5G traffic scheduler236, radio resource control (RRC) layer238, 2G RRC240, 3G RRC242, 4G RRC244, 5G RRC246, and network energy savings in multi-RAT networks component248(which can be similar to network energy savings in multi-RAT networks component108ofFIG.1).

As mobile network operators prepare to upgrade their existing networks to 5G NR releases (e.g., Release 17), pre-existing generations of 2G through 4G can still be prevalent. While network energy savings (NES) can be an important goal for all these generations, inherent measures can have been put in place for monitoring and optimization of energy usage in later 3GPP releases that can be largely unavailable in previous generations. Nonetheless, comprehensive measures can be required to ensure a highest possible energy efficiency (or increased energy efficiency) for M-RAT architectures that use an enhanced spectral efficiency of 5G, as well have a wider coverage afforded by previous generations. There can be different approaches to co-locate equipment for M-RATs, and the examples described herein can generally relate to a general case, where each technology uses a different frequency band, and, consequently, a dedicated single or multi-band antenna can be employed for each standard, or alternatively there may be dynamic sharing of spectrum (DSS) between different RATs. While traditional base station design can be more monolithic, later standards can favor a more distributed approach, with an introduction of remote radio heads (RRH), pooled baseband processing units (BBU), and an advent of cloud-RAN (C-RAN). A macro-base station can generally comprise a power supply unit (PSU), a transceiver, and feeder cables for the antenna (in these examples, an antenna can be referred to as a separate entity due to its minimal direct role in energy consumption). Each of these elements can potentially be responsible for a significant amount of energy consumption. The transceiver can manage input and output signals, and it can, in turn, mainly be composed of three sub-elements: a BBU that is configured to perform digital signal processing, a radio frequency (RF) component that is configured to convert digital signals into analog signals and vice versa, and a PA that is configured to amplify the signal for transmission through the antenna. The latter can be reported to be one of the main sources of energy consumption in a base station that can reportedly account for 60% or more of the total energy consumption of the RAN. Each base station can additionally have its backhaul link that connects with a core network.

An energy consumption scenario by a base station can become slightly better when considering multi-RAT architectures that host different technologies at the same time. Operating an M-RAT base station instead of singular monolithic base stations can yield some degree of energy savings as by using several transceivers at once, a single modular PSU can, on average, operate with higher efficiency. Furthermore, C-RANs can comprise architectures where the BBUs are pooled and centralized in a remote location. Centralizing BBUs can eliminate a need to have power-hungry cooling systems in each base station or in each RRH baseband site, which, together with an overall radio site simplification, can reduce OPEX for new sites, as well. C-RAN can also allow for increasing an overall energy efficiency (EE) by sharing computational resources available to signal processing techniques, load balancing among cells, and an implementation of multicell-based techniques.

In some examples, an overall architecture of the access protocol stack can be approximately the same for all cellular technologies. A mobile protocol stack from the lowest to the highest layer can be composed of a physical (L1), a data link (L2), and the network layer (L3). While L1 can largely be composed of an RF component and a series of signal processing components for baseband processing, L2 can be comprised of MAC, radio link control (RLC), and packet data convergence protocol (PDCP). Finally, L3 can handle control concerned with resource management using RRC with control plane (CP) and user plane (UP).

A C-RAN paradigm can provide a functional split between RF and PHY, allowing for maximum remote aggregation. A common public radio interface (CPRI) that is designed to transport sampled radio waveforms to the radio unit can also consume energy that is proportionate to the amount of in-phase quadrature (I/Q) samples that the radio unit uses to transport. Centralizing BBUs can increase their EE, however, a higher fronthaul capacity can generally also mean a higher fronthaul power drain. Transport of I/Q symbols can require a high continuous bitrate. In addition, as the bitrate scales linearly with the number of antennas, it places a higher burden in multiple input multiple output (MIMO) systems. For all these aspects, M-RAT deployments can complicate things further, as signals of multiple RATs can be multiplexed on the fronthaul.

5G can involve an even more careful consideration of the constraints. In 5G NR a CU, a DU, and multiple RUs can be considered to be a central architecture. Consequently, a link between the RU and DU can be referred to as a fronthaul, and a link between the DU and CU can be referred to as a mid-haul. In some examples, a CU can talk to multiple DUs, as well. 5G network deployments can be deployed as per a 3GPP Non-Stand Alone (NSA) architecture, where a NR base station coexists with a LTE base station, and operates under a 4G core. A 3GPP SA architecture, on the other hand, can allow for independent 5G deployments. While both the NSA and SA architectures can be compatible with M-RAT deployments, an NSA version can imply that an LTE radio acts as a master node, and determines the behavior of the associated NR components.

RAN as a Service (RANaaS), whereby an access network is seen more as a service-centric entity can be applicable also to NSA architectures. A RANaaS architecture can also be used to reduce an overall energy consumption while being able to guarantee strict performance guarantees (such as relating to latency). The positions of functional splits can play a role, and an ability to place them dynamically, by actively assigning functionalities among network levels based on current service requirements/traffic demands can give rise to a dynamic network architecture that can be leveraged. A next generation M-RAT implementation with a RU, DU, and CU architecture can facilitate energy savings through maximum function aggregation without committing to a fixed architecture. For example, in some examples baseband functions can be divided between DUs and RUs. Radio items can interact with a virtual BBU, regardless of whether baseband services are provided by a local, middle-tier, or remote unit, or a combination of the three.

Some base stations can need to be always on in order to signal their presence and monitor the radio channel to be visible by UEs. While some power consumption reduction strategies for base stations, such as sleep procedures (or low power consumption states) can be used in prior approaches, they have had limited efficacy far. Upgrading network deployments per newer cellular technology generations can lead to further network densification, as increased throughputs for UEs can usually be attained by progressively improving link budgets by limiting coverage and creating cells with smaller radius. However, this can lead to further randomness in traffic patterns, making the need for effective sleep mechanisms even more acute.

A multi-objective multi-constraint optimization for energy saving in a multi-RAT environment can be as follows. In some examples, one or more of the following can be considerations in such a multi-objective multi-constraint optimization. In a Multi-RAT environment deriving optimal network operational policy considering user association, power control, along with optimal resource allocation, can be a problem. A network cost/utility function prioritizing EE under the constrained resource utilization can be a problem. Moreover, optimal RAT selection in a Multi-RAT scenario comprising 5G NR, LTE, WCDMA, and GPRS RATs through traffic offloading/steering for EE maximization (or savings) can be a problem. Optimally servicing mobile network traffic via directing the request to the appropriate RAT can help with a maximization (or improvement) of an energy efficiency focused network utilization metric. Optimal RAT selection through traffic offloading, in a Multi-RAT network under the constraint of user quality of experience (QoE), therefore, can benefit from a solution.

Regarding these problems, one problem with prior approaches can be a lack of integrated solutions for Multi-RAT EE. While, according to some prior approaches, RAT selection has been considered for wireless devices under various constraints, it can be that there has been little work for optimizing resource selection in a network-centric way for a multi-RAT solution. For the prior approaches that have been made, it can be that the energy efficiency of the base stations has rarely been a criterion, as the emphasis has been on the device side, i.e., the selected RAT is considered to be optimal for usage by the device itself. With a growing concern regarding RAN energy consumption, solutions are needed that solve this problem from a RAN-centric view as well, giving network operators the control over which of the RAT options (available to service the devices) is used at a given time and what resources, computational and otherwise, are allocated to it. Use cases, such as driver assistance through vehicular telematics, mobile health, industrial internet, and the like, can be dependent on wide coverage low power services that are reliable, while high resolution video streaming, real-time content transfer, etc., can require high bandwidth with some error resilience at the application layer. These contrasting requirements can make such decision making even more difficult, and out of bounds for human-level response. An aspect to solve such disparate requirements in an energy-optimal way can be to automate the allocation process, taking into consideration key constraints, and synthesizing a final policy based on a multitude of relevant network data.

Another problem with prior approaches relates to network selection as per EE criteria under multi-RAT constraints. Formulating a network selection problem as an optimization problem with reasonable complexity can be a problem. Obtaining an optimal solution, subject to different networks, applications, and power constraints can lead to a non-deterministic polynomial-time (NP)-hard problem. Moreover, since network resources and channel characteristics can be inherently time-varying, along with the computational resources that can be used to determine their optimal usage, prior optimization approaches can be computationally prohibitive, with an added risk of providing obsolete resource policy recommendations, necessitating a use of online optimization approaches.

The present techniques can be implemented to facilitate an energy consumption optimal reinforcement learning (RL) based framework that is configured to not only choose the network resource that is optimal for the given demand, but also prioritize one that uses a least amount of energy in providing the requisite throughput at an optimal coverage level for the targeted block error rate (BLER).

Note that in the process, in addition to selecting the optimal RAT, the policy recommendation can also optimize scheduling within the selected RAT with a set of dimensions/parameters that pertain only to that RAT. To that end, the present techniques can be implemented to facilitate a framework to resolve the optimal user association problem for various RATs. Additionally, and in contrast to prior approaches, the present techniques can take a network centric view of the problem, rather than a user centric view, to be able to address a base station energy efficiency issue.

Another problem with prior approaches relates to a lack of optimal criteria to shut down RATs per traffic demand.

An aspect of EE Multi-RAT systems can be to shut off parts of the system that are either not in use, or are not a most energy efficient option considering the demand. Determining this can depend on a combination of factors, such as providing continued connectivity, efficient transmission and leveraging channel conditions to use a RAT (such as a best possible RAT) to address the traffic request. For example, while 2G and 4G system can require more of always ON approach to provide connectivity, control signaling overhead can be relatively higher compared to 5G NR. On the other hand, from a coverage standpoint, 2G systems can provide wider coverage, and, if using a different narrow frequency band, can provide baseline connectivity for a wide set of users by acting as an underlay system with the ability to handle packetized transmission at a very low bit rate.

In general, switching RAT(s) on/off too frequently can be impractical, since the switching process itself can take time, and can require additional compute resources, as well as protocol overhead. Therefore, an approach can be to do the active RAT selection on a large timescale, e.g., based on an average daily traffic load profile obtained from long-term observations. This approach can be optimized, or improved, through offline training. Nonetheless, with a dynamic nature of future applications there can be deviation from assumptions under which training is performed to an actual operational environment. For example, when the average traffic load is high, it can be that more active RATs (some operating in parallel) are selected, while when the average traffic becomes relatively low, a few RATs can be switched off, which can make sense from an EE enhancement perspective. A number of candidate RATs in practical systems can usually be small, and therefore such a combination of offline learning, which can lead to a look-up table for selecting optimal active RATs, and online learning that avoids an exhaustive search, can be made computationally cheap.

The present techniques can be implemented as follows, such as to optimize operations of a base station under a multi-RAT configuration, which can focus on energy efficiency while also additionally ensuring optimal resource allocation, optimal RAT selection, and a simplification of network control through a unified view.

A high-level functional diagram embedding the inventive aspects of the current disclosure is shown inFIG.2. In particular, when a UE service request is received, a CU/DU server can host what can be referred to as a service request staging and preprocessing component. This component can be configured to use a reported reference signal received power (RSRP), or received signal strength indicator (RSSI), for the relevant RATs that it can connect to for pre-processing purposes, with a first level of filtering performed by discarding the RATs that do not meet the required threshold for coverage. For UEs that are already connected but have a new service request (in examples where one UE is capable of running several applications simultaneously, one of which can require a new request to connect to the base station), signal-to-interference and noise ratio (SINR) can also be used for the preprocessing. For the RATs that meet the threshold, these metrics (data points) can be fed into the AI/ML engine for RAT selection component for decision making on an optimal RAT to schedule the UE on. The AI/ML policy engine can further be supported by the energy efficiency computation component that is configured to compute the energy efficiency on a per RAT basis, and is described further herein.

FIG.3illustrates an example system architecture300for AI/ML-based RAT selection based on energy efficiency, and that can facilitate network energy savings in multi-RAT networks, in accordance with an embodiment of this disclosure. In some examples, part(s) of system architecture300can be used to implement part(s) of system architecture100ofFIG.1.

Additionally, the AI/ML policy engine can also access a database that maintains a set of transmission thresholds—e.g. minimum RSRP/RSSI levels for each of the RATs for the selection and use of a certain target spectral efficiency (through a combination of modulation and coding schemes/rates)—that can be used to determine whether a given UE can be scheduled on a given RAT for the requested throughput. These thresholds can be dynamic due to a time-varying nature of the channel, and can therefore be updated periodically based on information received from the link adaptation components corresponding to each RAT. The AI/ML policy engine can work in tandem with other components that can provide the requisite telemetry or execute ML based prediction algorithms themselves to characterize a future state of the network.

In some examples, there is little to no interference between RATs, since different RATs operate in different frequency bands. Given a network-centric approach, in some examples, the CU can host a central control component that is configured to perform radio resource management (RRM) for multi-carrier transmission, and can facilitate parallel transmission among RATs to split the servicing of a demand. For parallel transmission, a desired data stream can be split into multiple sub-streams for multiple RATs to transmit simultaneously. In some examples, this can be more likely to happen between RATs that share a common air interface such as 4G and 5G based RATs in SA or NSA mode as both use orthogonal frequency-division multiple access (OFDMA), and in some examples, have common channel bandwidths and sub-carrier spacing, as well.

The present techniques can be implemented to facilitate energy efficiency optimal user association for Multi-RAT. An optimization according to the present techniques can be an overall energy efficiency optimization when UEs are serviced in a multi-RAT scenario. RAT selection can consider a UE-centric utility function that can maximize a QoS of the UE, and can lead a user association for a greatest benefit of the UE. However, in some examples, this can be counterproductive in two ways: (1) It can be that it does not always lead to a solution that reduces the network energy consumption, and (2) It can also lead to a highly unbalanced network that allows the greedy approach of UEs to determine the network operational state.

The present techniques can be implemented to facilitate a maximization of a utility function with network energy consumption at its core. A single agent RL environment can be implemented, where the agent tries to learn an optimal policy from interactions with the environment, aiming to maximize its reward. This can be possible due to a network-centric RAT selection approach that can be implemented according to the present techniques. Conversely, if the UEs were to make the decision of RAT selection, then it could be that a multi-agent reinforcement learning environment would be considered, which can lead to an action and state space that is more complicated, and it can be that fully distributed approaches do not guarantee convergence to equilibrium states, in addition to being slow, and requiring high exploration times. An objective function can be implemented that is optimized through reinforcement learning. The state space can be comprised of:

The set of RATs, denoted by NRAT

The power consumption of each of the RATs: PRATkfor the kthRAT

The channel resources of the kthRAT (for, e.g., a number of subcarriers with a given MCS for an OFDMA-based system, Ckis subject to a maximum resource limit CMAXk).

With overall energy efficiency being defined as:

can be indicator functions and Pk0denotes energy consumed by the kthRAT in idle mode, that is, no traffic mode being carried. Selection can be subject to the following further constraints:Constraint C0: Σuγj,k≤CkMAX; ∀u (which can ensure that resources assigned to users cannot exceed maximum resources available for all UEs denoted by u)Constraint C1: Qmin(u)≤Qn∈NRAT(u)≤Qmax(u): can guarantee a desired KPI level (Q) for user u, connect to the RATn∈NRAT, in the network is satisfied with KPI tolerance bounds Qmin(u) and Qmax(u). In some examples, KPI can be delay, throughput, or another parameter that is selected by a service provider per SLA.

The present techniques can also be implemented to facilitate a RL framework for optimal RAT selection. Large portions of an IoT traffic, such as machine to machine (M2M) and vehicular telematics, can use 2G processing. 5G rollout can continue to be following a phased approach in different markets with an NSA mode being a primary approach, whereby the protocol processing control is performed with a 4G core, and 5G access features are supported on top of the core. For such multi-RAT deployments to be economically viable, it can be that a network equipment provider (NEP) supports approaches to deal with complexities of supporting multiple RATs with minimized energy usage.

Moreover, since network resources and characteristics can vary with time, as well as the edge resources, it can be that prior optimization approaches can be computationally expensive, and online optimization approaches, such RL, can be implemented. With RL environments, deep q-networks (DQN) (policy or value based) can be implemented for optimization, and the state space, action and rewards can be defined accordingly. According to the present techniques, they can be mapped as follows.

The state space can comprise a set of RATs (NRAT) and the available resource for the RAT(CkMAX). There can be an energy efficiency associated with the use of the resource within that RAT that can be derived from the energy efficiency compute model. This can take into account predicted traffic for that time interval for each RAT, and the use of a variable bias on the shared PA.

The action space can be nested in the sense that can take into account both the selection of a given RAT, and allocated resources within that RAT. This can be, for example, the set of sub-carriers that allow for the highest spectral efficiency while maintaining the target BLER for that RAT. Furthermore, for an operational network, the RL agent can continually evaluate the possibility of transferring a UE to the most energy optimal RAT while in connected state. This can occur, for example, in the case where a 5G NR connected UE is transferred to a 3G RAT to create more DTX opportunities for the 5G RAT.

Reward shaping can be implemented as follows. The RL agent can receive a positive reward if the energy consumption of the multi-RAT base station achieves a value lower than a threshold εMAθ, and a negative reward if the energy efficiency goes up compared to the time. Here εMAθcan comprise the time averaged energy efficiency for a particular load θ.

FIG.4illustrates an example power consumption modeling for a multi-RAT architecture with a shared PA, and that can facilitate network energy savings in multi-RAT networks, in accordance with an embodiment of this disclosure. In some examples, part(s) of power consumption modeling400can be used to implement part(s) of system architecture100ofFIG.1.

System architecture400comprises power_model_RAT1402(4G/5G NSA RAT stack), power_model_RAT2404(2G/3G RAT stack), power_model_RAT3406(5G NR SA RAT stack) with various sleep modes that have corresponding power consumptions denoted bysleep_mode_0-power408, sleep_mode_1-power410, sleep_mode_2-power412, sleep_mode_3-power414, sleep_mode_0-power416, sleep_mode_1-power418, sleep_mode_0-power420, sleep_mode_1-power422, sleep_mode_(N−1)-power424, sleep_mode_N-power426, and network energy savings in multi-RAT networks component428(which can be similar to network energy savings in multi-RAT networks component108ofFIG.1).

The present techniques can be implemented to facilitate energy efficient computation with a shared PA approach. In addition to the RAT selection, there can be use of components inFIG.3. One of these components is the ML-based traffic predictor that can internally use a long-short term memory (LSTM) based architecture on a per RAT basis to predict traffic that each RAT can be servicing over time. This can be distinct from a single-RAT based predictor where all traffic is governed by the resources available for that RAT. A multi-RAT base station can take into account the traffic that is serviced on an individual RAT basis. Furthermore, the energy efficiency compute component can house power consumption models for individual RATs that take into account the ability or lack thereof in each of the models to operate in a lower power consumption state, as shown inFIG.3.

In this example, there are three different power models as, denoted by RAT1, RAT2, and RAT3 whereby each has a different low power consumption state that it can use, and a variation exists both in the power usage in that state and the total number of such possible states. For example, RAT2 can model a 2G or 3G RAT whereby two sleep modes are available, of which one where it is completely switched off (and thus provides no connectivity), and second is where control-plane traffic is flowing to maintain coverage and no data is being directed to due to the high bandwidth requirements of the requesting UEs.

FIG.5illustrates an example system architecture500for a load-balanced multi-RAT base station selection with energy efficiency, and that can facilitate network energy savings in multi-RAT networks, in accordance with an embodiment of this disclosure. In some examples, part(s) of system architecture500can be used to implement part(s) of system architecture100ofFIG.1.

System architecture500comprises AI/ML based traffic load predictor502, which in turn comprises of traffic load predictor RAT 1504, traffic load predictor RAT 2506, traffic load predictor RAT 3508for individual RATs, energy consumption determination of the multi-RAT base station510, RL agent for RAT selection512, multi-RAT power consumption model514(which can be similar to system architecture400ofFIG.4), and load balanced RAT selection with enhanced energy efficiency516.

The following are examples of how the present techniques can help in mitigation of various aspects related to energy efficiency and resource allocation in contemporary multi-RAT networks.

One example of how the present techniques can help in mitigation of various aspects related to energy efficiency and resource allocation in contemporary multi-RAT networks can relate to load balancing. For sites with multiple RATs but separate PAs per RAT, load balancing between the RATs can be an alternative. Where 4G and 5G NR can reduce energy consumption by use of sleep modes, one approach can be to enhance an overall energy performance by transferring as much traffic as possible to a 3G RAT, for example. This can allow the 5G NR SA, 5G NR-NSA to be put in deeper hardware sleep states, followed by the 4G-LTE RAT, where most of the traffic is non-packet or short-packet bursty. For 3G/HSPA the traffic can potentially increase to accommodate all the 5G NR and LTE traffic, so balance can be maintained. Furthermore, this can enhance the opportunities for cell DTX, reducing the power consumption in accordance with hardware improvements as described herein.

It can be seen in a deployment with several multi-RAT base stations that, if the traffic is always steered to the same base station with a certain RAT that can best satisfy the desired QoS, it can potentially devolve into an unbalanced load distribution. This can eventually degrade important network KPIs such as delay, packet drops, etc., and thus have an overall negative impact on the system throughput. However, in a RL environment according to the present techniques, the base station can be empowered either themselves to make these decisions (considering base station load conditions explicitly), or it can be enforced through a RIC that is configured to predict a load on multiple base stations based on both the current load conditions as well as an aggregated version of the traffic prediction component shown inFIG.2. Without such configuration according to the present techniques, other than network performance degradation, the network can even exhibit potential oscillation and instability.

Moreover, in examples where this decision making is left to the UEs to be carried out in a distributed fashion, then it can be that most existing distributed RAT selection approaches require that all users know the historical selection policy/decisions of other users. The knowledge of the instantaneous rates of the other users can come at a cost of substantially increased complexity, signaling, and communication load. Conversely, when the problem is addressed within a single agent reinforcement learning approach, it can be that most of the communication is internal to the base station, and the RL agent is able to assimilate several network measurements and synthesize an intelligent policy that can significantly minimize the signaling overhead.

In contrast to a conventional traffic steering problem where load balancing is done to direct the traffic to the correct base station, in some examples, the present techniques can be implemented to facilitate directing traffic to the correct RAT considering two constraints:

Balanced load distribution amongst different RATs based on the traffic demand; and

Re-direct demand to the RAT that minimizes a global energy consumption.

An example capable of achieving this is shown inFIG.4. The main energy efficiency compute block can derive its inputs from two blocks: (a) the ML-based traffic predictor and the (b) Multi-RAT power computation. In a multi-RAT base station, a traffic predictor can base its prediction on a percentage of total traffic that has been redirected to each base station. In this example, there can be a dedicated traffic predictor for each RAT. A history of traffic states of each predictor can also be influenced by how previous states can represent a result of a RAT selection that was also influenced by energy efficiency considerations. This can be highlighted when considering that the RL agent for RAT selection inFIG.4can derive its inputs from both the energy consumption metrics on a per RAT basis for the predicted traffic, as well as the load experience by each RAT if the traffic were to be directed to it. Therefore, the RL agent can be able to respond with a policy that can explicitly take into account the dynamic operational environment of the multi-RAT base station.

Another example of the present techniques can relate to resource allocation for energy efficiency maximization in multi-rate transmission. In some examples, approaches according to the present techniques can be used to further optimize the energy efficiency within the RAT. For example, even when allocating resources between two OFDMA based RATs, it can be possible that due to the operational requirements and current load on a given RAT, one RAT is more optimal than the other from the perspective of a given network metric such as throughput, energy efficiency maximization, or overall load balancing. This can occur due to both the time varying nature of the traffic itself, and the variation in channel conditions over different frequency regions that each RAT operates in. A benefit of the present techniques that can be had by enabling an RL agent to handle the activation of particular energy saving features can be that, when servicing a UE request, the resource request can be split across RATs. This can lead to an expanded resource scheduling sub-space for the RL agent, whereby a more energy optimal operating point can be found by allowing for parallel transmission on two RATs simultaneously in order to meet a desired QoS of the UE. This can help in two ways: (a) more UEs can be admitted and serviced thereby increasing the achieved throughput of the network, and (b) soft migration of UEs to a single RAT if the energy optimization policy recommends switching off a RAT completely, such the degradation in QoS is gradual.

Example Process Flows

FIG.6illustrates an example process flow600that can facilitate network energy savings in multi-RAT networks in base stations, in accordance with an embodiment of this disclosure. In some examples, one or more embodiments of process flow600can be implemented by network energy savings in multi-RAT networks component108ofFIG.1, or wireless communications operating environment1000ofFIG.10.

Process flow600begins with602, and moves to operation604.

Operation604depicts receiving a service request from a user equipment. Using the example ofFIG.1, user equipment104can make a service request to base station102.

After operation604, process flow600moves to operation606.

Operation606depicts, based on the service request, determining respective signal strengths of respective radio access technologies of multiple radio access technologies being used for cellular broadband communications. That is, a base station (e.g., base station102ofFIG.1) can be a multi-RAT base station.

After operation606, process flow600moves to operation608.

Operation608depicts identifying a subset of the respective radio access technologies for which a signal strength criterion is satisfied. That is, initially in selecting a RAT for the user equipment, possible RATs can be filtered, such as based on having a sufficient RSRP or RSSI that the user equipment can connect to for pre-processing purposes. RATs that do not meet a threshold for coverage can be discarded from consideration.

In some examples, at least one of the respective signal strengths comprises a reference signal received power value. In some examples, at least one of the respective signal strengths comprises a received signal strength indicator value.

In some examples, when receiving the service request from the user equipment occurs, at least some of the cellular broadband communications are being conducted with the user equipment for usage of a first application of the user equipment, wherein the service request corresponds to a second application of the user equipment, and wherein at least one of the respective signal strengths comprises a signal-to-interference and noise ratio value. That is, for UEs that are already connected but have a new service request (where a UE is capable of running several applications simultaneously, one of which provides a new request to connect to a base station), SINR can be used in operation608as the signal strength metric.

After operation608, process flow600moves to operation610.

Operation610depicts processing a subset of the respective signal strengths corresponding to the subset of the respective radio access technologies using a machine learning model to determine a selected radio access technology of the multiple radio access technologies, wherein the machine learning model is trained to determine the selected radio access technology based on an energy efficiency metric of the system and based on resource allocation of the system. That is, for the RATs that meet the threshold of operation608, these metrics can be provided as input to a machine learning model for decision-making on a RAT to schedule the UE on.

In some examples, this can involve using overall energy efficiency of multi-RAT base station as an element of RAT selection. In some examples, the selection of the RAT can also be subject to QoS constraints.

In some examples, the machine learning model is trained to favor increasing the energy efficiency metric of the system while satisfying the resource allocation of the system. That is, the machine learning model can be configured to focus on energy efficiency while also ensuring optimal resource allocation, or resource allocation that satisfies a criterion related to resource allocation.

In some examples, the machine learning model is trained to determine the selected radio access technology based on a service level agreement that corresponds to a performance metric applicable to the cellular broadband communications. That is, the machine learning model can be trained to determine on which mode to operate each RAT when servicing a certain set of requests so as to not violate SLAs, using a network-centric approach.

In some examples, the machine learning model is trained to determine the selected radio access technology based on satisfying at least one of a requested quality-of-service metric associated with the user equipment, a data rate associated with the user equipment, or a contract associated with the user equipment. That is, the machine learning model can be trained to determine an optimal association of UEs with the base station/RAT based on certain criteria (e.g., requested QoS, data rate or other performance contracts) for efficient utilization of the available radio resources with an energy efficiency optimization constraint.

After operation610, process flow600moves to operation612.

Operation612depicts communicating with the user equipment via the selected radio access technology. That is, the base station can schedule the UE on the RAT determined in operation610.

In some examples, the cellular broadband communications are first cellular broadband communications, and operation612comprises updating the offline trained machine learning model according to online reinforcement learning based on feedback resulting from in-field operations of conducting second broadband cellular communications. That is, online learning can be implemented on the pre-trained machine learning model to incorporate feedback received from in-field operations to update the model to improve an objective of energy savings.

FIG.7illustrates an example process flow700that can facilitate network energy savings in multi-RAT networks in base stations, in accordance with an embodiment of this disclosure. In some examples, one or more embodiments of process flow700can be implemented by network energy savings in multi-RAT networks component108ofFIG.1, or wireless communications operating environment1000ofFIG.10.

Process flow700begins with702, and moves to operation704.

Operation704depicts receiving a service request from a user equipment, wherein the service request corresponds to facilitating cellular broadband communications. In some examples, operation704can be implemented in a similar manner as operation604ofFIG.6.

After operation704, process flow700moves to operation706.

Operation706depicts, based on the service request, determining respective signal strengths of respective radio access technologies of a group of multiple radio access technologies usable for the cellular broadband communications. In some examples, operation706can be implemented in a similar manner as operation606ofFIG.6.

After operation706, process flow700moves to operation708.

Operation708depicts identifying a subset of the respective radio access technologies for which corresponding signal strengths satisfy a signal strength criterion. In some examples, operation708can be implemented in a similar manner as operation608ofFIG.6.

After operation708, process flow700moves to operation710.

Operation710depicts processing the corresponding signal strengths of the subset of the respective radio access technologies with a trained machine learning model to determine a selected radio access technology of the group of multiple radio access technologies, wherein the trained machine learning model is configured to determine the selected radio access technology based on an energy efficiency metric of the system and based on resource allocation of the system. In some examples, operation710can be implemented in a similar manner as operation610ofFIG.6.

In some examples, the trained machine learning model is a pre-trained machine learning model, and operation710comprises training a machine learning model to produce the pre-trained machine learning model according to a single-agent reinforcement learning process, wherein a state space of the single-agent RL process comprises the group of multiple radio access technologies, respective power consumption values of respective multiple radio access technologies of the group of multiple radio access technologies, and respective channel resources of the respective multiple radio access technologies. That is, single-agent RL can be implemented, where the agent attempts to learn an optimal policy from interactions with its environment, aiming to maximize its reward. A state space for the RL process can comprise the set of RATs, denoted by NRAT; the power consumption of each of the RATs (PRATkfor the kthRAT); and the channel resources of the kthRAT (for, e.g., a number of subcarriers with a given MCS for an OFDMA-based system, Ckis subject to a maximum resource limit CMAXk).

In some examples, operation710comprises maintaining a data store that comprises respective transmission thresholds for the respective radio access technologies, and respective target spectral efficiencies of the respective radio access technologies, and determining the selected radio access technology of the group of multiple radio access technologies is based on determining that the selected radio access technology is configured to schedule the user equipment to use the selected radio access technology to achieve a requested throughput. In some examples, operation710comprises, in response to an update criterion being determined to be satisfied, updating the respective transmission thresholds for the respective radio access technologies based on information received from respective link adaptation components of the respective radio access technologies. That is, the machine learning model can have access to a database that maintains a transmission thresholds (e.g., minimum RSRP/RSSI levels for each of the RATs for the selection and use of a certain target spectral efficiency (through a combination of modulation and coding schemes/rates)) that can be used to determine if a given UE can be scheduled on a given RAT for the requested throughput. These thresholds can be dynamic due to a time-varying nature of the channel, and so can be updated periodically based on information received from the link adaptation modules corresponding to each RAT.

In some example, operation710comprises training a machine learning model to produce the trained machine learning model according to a reinforcement learning process, wherein the reinforcement learning process is configured to receive a positive reward when an energy consumption associated with a state of the reinforcement learning process is less than an average energy efficiency for a load, and wherein the reinforcement learning process is configured to receive a negative reward when the energy consumption associated with the state of the reinforcement learning process is greater than the average energy efficiency for the load. That is, reward shaping can be used with a RL agent, where the RL agent receives a positive reward if the energy consumption of the multi-RAT BS achieves a value lower than a threshold εMAθ, and a negative reward if the energy efficiency goes up compared to the time. Here εMAθcan comprise the time averaged energy efficiency for a particular load θ.

After operation710, process flow700moves to operation712.

Operation712depicts communicating with the user equipment using the selected radio access technology. In some examples, operation712can be implemented in a similar manner as operation612ofFIG.6.

In some examples, the selected radio access technology is a first selected radio access technology. In such examples, operation712can comprise, after determining the selected radio access technology of the group of multiple radio access technologies, determining, by the system and with the trained machine learning model, a second selected radio access technology of the group of multiple radio access technologies, wherein the second selected radio access technology corresponds to a better energy efficiency criterion with respect to the user equipment than the first selected radio access technology, and transferring the user equipment from the first selected radio access technology to the second selected radio access technology. That is, for an operational network, an RL agent can continually evaluate a possibility of transferring a UE to a most energy optimal RAT while in a connected state. This can occur, for example, in a case where a 5G NR connected UE is transferred to a 3G RAT to create more DTX opportunities for the 5G RAT.

FIG.8illustrates an example process flow800that can facilitate network energy savings in multi-RAT networks in base stations, in accordance with an embodiment of this disclosure. In some examples, one or more embodiments of process flow800can be implemented by network energy savings in multi-RAT networks component108ofFIG.1, or wireless communications operating environment1000ofFIG.10.

Process flow800begins with802, and moves to operation804.

Operation804depicts, based on receiving a service request from a user equipment, determining respective signal strengths of respective radio access technologies of a group of multiple radio access technologies. In some examples, operation804can be implemented in a similar manner as operations604-606ofFIG.6.

After operation804, process flow800moves to operation806.

Operation806depicts identifying a subset of the respective radio access technologies for which a corresponding subset of the respective signal strengths satisfies a signal strength criterion. In some examples, operation806can be implemented in a similar manner as operation608ofFIG.6.

After operation806, process flow800moves to operation808.

Operation808depicts processing the respective signal strengths of the subset of the respective radio access technologies with an artificial intelligence model to determine a selected radio access technology of the group of multiple radio access technologies, wherein the artificial intelligence model is trained to determine the selected radio access technology based on an energy efficiency metric of the system and based on resource allocation of the system. In some examples, operation808can be implemented in a similar manner to operation610ofFIG.6.

In some examples, the artificial intelligence model comprises a group of long-short term memory models, and wherein respective long-short term memory models of the group of long-short term memory models correspond to the respective radio access technologies. That is, the artificial intelligence model can comprise an ML-based traffic predictor that uses a LSTM-based architecture on a per RAT basis to predict the traffic that each RAT may be servicing over time.

In some examples, operation808comprises storing respective power consumption models for the respective radio access technologies, wherein the respective power consumption models comprise respective amounts of power consumed in respective low-power states, in addition to the nominal power consumption when no low-power states are activated for respective RAT.

In some examples, operation808comprises storing respective power consumption models for the respective radio access technologies, wherein at least two power consumption models of the respective power consumption models differ in terms of a number of power consumption states.

That is, in determining a RAT, different power models for different RATs can be considered, where there can be a variation in both a power usage in a given state and a total number of such possible states.

In some examples, the respective radio access technologies correspond to use of respective power amplifiers that have different energy consumption, and the artificial intelligence model is configured to perform load balancing amongst the different radio access technologies that comprise the user equipment. That is, in sites with multiple RATs but separate PAs per RAT, load balancing between the RATs can be performed.

In some examples, a first base station is configured to provide the group of multiple radio access technologies, and a radio access network intelligent controller is configured to perform load balancing among respective base stations of a group of base stations that comprises the first base station based on current load conditions and a traffic prediction model. That is, a RIC can predict a load on multiple base stations and load balance across the base stations.

After operation808, process flow800moves to operation810.

Operation810depicts communicating with the user equipment using the selected radio access technology. In some examples, operation810can be implemented in a similar manner as operation612ofFIG.6.

FIG.9illustrates an example process flow900for training a model with a reinforcement learning agent, and that can facilitate network energy savings in multi-RAT networks in base stations, in accordance with an embodiment of this disclosure. In some examples, one or more embodiments of process flow900can be implemented by network energy savings in multi-RAT networks component108ofFIG.1, or wireless communications operating environment1000ofFIG.10.

It can be appreciated that the operating procedures of process flow900are example operating procedures, and that there can be embodiments that implement more or fewer operating procedures than are depicted, or that implement the depicted operating procedures in a different order than as depicted. In some examples, process flow900can be implemented in conjunction with one or more embodiments of one or more of process flow600ofFIG.6, process flow700ofFIG.7, and/or process flow800ofFIG.8.

Process flow900begins with902, and moves to operation904.

Operation904depicts defining a state space. In some examples, the state space can comprise a set of RATs (NRAT) and the available resource for the RAT(CkMAX). There can be an energy efficiency associated with the use of the resource within that RAT that can be derived from the energy efficiency compute model. This can take into account predicted traffic for that time interval for each RAT, and the use of a variable bias on the shared PA.

After operation904, process flow900moves to operation906.

Operation906depicts defining actions. In some examples, an action space can be nested in the sense that can take into account both the selection of a given RAT, and the resources to be allocated within that RAT. This can be, for example, the set of sub-carriers that allow for the highest spectral efficiency while maintaining the target BLER for that RAT. Furthermore, for an operational network, the RL agent can continually evaluate the possibility of transferring a UE to the most energy optimal RAT while in connected state. This can occur, for example, in the case where a 5G NR connected UE is transferred to a 3G RAT to create more DTX opportunities for the 5G RAT.

After operation906, process flow900moves to operation908.

Operation908depicts defining reward shaping. In some examples, reward shaping can be implemented as follows. The RL agent can receive a positive reward if the energy consumption of the multi-RAT base station achieves a value lower than a threshold εMAθ, and a negative reward if the energy efficiency goes up compared to the time. Here εMAθcan comprise the time averaged energy efficiency for a particular load θ.

After operation908, process flow900moves to operation910.

Operation910depicts performing RL training. This can comprise performing RL training on a ML model using the state space of operation904, the action space of operation906, and the reward shaping of operation908. The trained ML model can be used to select a RAT in a multi-RAT scenario, as described herein.

Example Operating Environment

In order to provide additional context for various embodiments described herein,FIG.10and the following discussion are intended to provide a brief, general description of a suitable wireless communications operating environment in which the various embodiments of the embodiment described herein can be implemented.

For example, parts of wireless communications operating environment can be used to implement one or more embodiments of base station102and/or user equipment104ofFIG.1.

In some examples, wireless communications operating environment can implement one or more embodiments of the process flows ofFIGS.6-9to facilitate network energy savings in multi-RAT networks in base stations.

While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules and/or as a combination of hardware and software.

FIG.10illustrates an example block diagram of a wireless communications operating environment1000operable to execute an embodiment of this disclosure. A UE of UE(s)1004A, UE(s)1004B, and/or UE(s)1004N can generally comprise a device used by an end user to access a communications network. A UE can be configured to receive messages from communications network1006, which can be, for instance, a global communications network such as the Internet.

Messages sent from A UE can be received and processed by core network1008, which can comprise components of a 3G, 4G, LTE, 5G, or other, wireless communication network. Core network1008can be configured to establish connectivity between a UE and communications network1006, such as through facilitating services such as connectivity and mobility management, authentication and authorization, subscriber data management, and policy management. Messages sent between a UE and communications network1006can propagate through CU1010, DU1012, RU1014, and antenna1016A, antenna1016B, or antenna1016N.

CU1010can be configured to process non-real-time RRC and PDCP communications. DU1012can be configured to process communications transmitted according to RLC, MAC, and PHY layers. RU1014can be configured to convert radio signals sent to antenna1016from digital packets to radio signals, and convert radio signals received from antenna1016from radio signals to digital packets.

Antenna1016A, antenna1016B, and antenna1016N (which can comprise a transceiver) can each be configured to send and receive radio waves that are used to convey information. Antenna1016A can facilitate LTE communications; antenna1016B can facilitate 5G communications; and antenna1016N can facilitate 4G communications. The use of these multiple radio-access technologies to facilitate communications can comprise a multi-RAT environment.

CONCLUSION

In the subject specification, terms such as “datastore,” data storage,” “database,” “cache,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It will be appreciated that the memory components, or computer-readable storage media, described herein can be either volatile memory or nonvolatile storage, or can include both volatile and nonvolatile storage. By way of illustration, and not limitation, nonvolatile storage can include ROM, programmable ROM (PROM), EPROM, EEPROM, or flash memory. Volatile memory can include RAM, which acts as external cache memory. By way of illustration and not limitation, RAM can be available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, the disclosed memory components of systems or methods herein are intended to comprise, without being limited to comprising, these and any other suitable types of memory.