Orchestrating MIMO operation of radios

Abstract

A method is provided for orchestrating MIMO operation of radios in a communications system. The method includes accessing (1400) data including historical MIMO configuration data for the radios; and predicting (1404) an estimated energy consumption for a first PRB utilization 5 of a first radio in a first sector. The method further includes predicting (1406), based on the estimated energy consumption, a historical energy consumption for a second PRB utilization of a second radio in a second sector. The method further includes deciding (1412) one of (i) to turn off a first MIMO configuration of the first radio and turn on a second MIMO configuration of the second radio when the historical energy consumption is less than or equal to the estimated energy 0 consumption, and (ii) to keep the first MIMO configuration of the first radio when the historical energy consumption is greater than the estimated energy consumption.

Description

ORCHESTRATING MIMO OPERATION OF RADIOSTECHNICAL FIELD

[0001] The present disclosure relates generally to computer-implemented methods by a network node for orchestrating multiple input multiple output (MIMO) operation of a plurality of radios in a communications system, and related methods and devices.BACKGROUND

[0002] Operators may be looking for new ways to reduce the energy consumption of a telecommunications network. For example, a rise of global challenges may make energy prices volatile which, in turn, can have a dramatic impact on an operator’s operational costs. To reduce these costs, there may be a need for improving network energy efficiency.SUMMARY

[0003] There currently exist certain challenges. While a MIMO sleep feature may exist, the feature may have limitations and also may not provide acceptable energy savings. For example, limitations may include a set point of capacity on radio units, a fixed timer for the MIMO sleep function, lack of efficient optimization techniques for controlling how often and when a radio unit is off, and / or delays in controlling a radio unit.

[0004] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges.

[0005] Some embodiments provide a computer-implemented method performed by a network node for orchestrating MIMO operation of a plurality of radios in a communications system. The method includes accessing data comprising historical MIMO configuration data for the plurality of radios; and predicting for a future time period, with at least one first machine learning (ML) model, an estimated energy consumption for a first physical resource block (PRB) utilization of a first radio in a first sector. The method further includes predicting, with a second ML model based on the estimated energy consumption of the first radio, a historical energy consumption for a second PRB utilization of a second radio in a second sector. The method further includes deciding one of (i) to turn off a first MIMO configuration of the first radio and to turn on a second MIMO configuration of the second radio when the historical energy consumption is less than or equal to the estimated energy consumption, and (ii) to keep the first MIMO configuration of the first radio when the historical energy consumption is greater than the estimated energy consumption.

[0006] Other embodiments provide a network node. The network node is configured to orchestrate MIMO operation of a plurality of radios in a communications system. The network node includes processing circuitry, and memory coupled with the processing circuitry. The memory includes instructions that when executed by the processing circuitry causes the network node to perform operations. The operations include to access data comprising historical MIMO configuration data for the plurality of radios; and predict for a future time period, with at least one first ML model, an estimated energy consumption for a first PRB utilization of a first radio in a first sector. The operations further include to predict, with a second ML model based on the estimated energy consumption of the first radio, a historical energy consumption for a second PRB utilization of a second radio in a second sector. The operations further include to decide one of (i) to turn off a first MIMO configuration of the first radio and to turn on a second MIMO configuration of the second radio when the historical energy consumption is less than or equal to the estimated energy consumption, and (ii) to keep the first MIMO configuration of the first radio when the historical energy consumption is greater than the estimated energy consumption.

[0007] Some embodiments provide a non-transitory computer readable medium including program code to be executed by processing circuitry of a network node configured to orchestrate MIMO operation of a plurality of radios in a communications system. Execution of the program code causes the program code to perform operations. The operations include to access data comprising historical MIMO configuration data for the plurality of radios; and predict for a future time period, with at least one first ML model, an estimated energy consumption for a first PRB utilization of a first radio in a first sector. The operations further include to predict, with a second ML model based on the estimated energy consumption of the first radio, a historical energy consumption for a second PRB utilization of a second radio in a second sector. The operations further include to decide one of (i) to turn off a first MIMO configuration of the first radio and to turn on a second MIMO configuration of the second radio when the historical energy consumption is less than or equal to the estimated energy consumption, and (ii) to keep the first MIMO configuration of the first radio when the historical energy consumption is greater than the estimated energy consumption.

[0008] Certain embodiments may provide one or more of the following technical advantage(s). Based on inclusion of historical MIMO configuration data and use of at least two ML models, control of a MIMO configuration may be improved which, in turn, may reduce power consumption. For example, in contrast to a fixed timer, the operations may allow for dynamic timer adjustment for MIMO turn on / offBRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate certain non-limiting embodiments of the present disclosure. In the drawings:

[0010] Figure 1 is a schematic drawing of a high level example of a multiple MIMO arrangement in accordance with some embodiments;

[0011] Figure 2 is a schematic drawing of an example of antenna muting where different antennas are turned on / off based on fixed parameters;

[0012] Figure 3 is a schematic drawing of an example of a general framework for a MIMO sleep control strategy in accordance with some embodiments;

[0013] Figure 4 is a schematic drawing of an example MIMO site configuration in accordance with some embodiments;

[0014] Figure 5 is a schematic drawing of an example data set from MIMO configuration files in accordance with some embodiments;

[0015] Figure 6 is a schematic drawing of an example of an overall structure and process in accordance with some embodiments;

[0016] Figure 7 is a schematic drawing of an example of operations of an optimizer of Figure 6 for determining optimized energy in accordance with some embodiments;

[0017] Figure 8 is a plot from an evaluation of a prediction of consumed energy in accordance with some embodiments;

[0018] Figure 9 is a plot from an evaluation in accordance with some embodiments;

[0019] Figure 10 is a plot showing an example of a static and dynamic variance in accordance with some embodiments;

[0020] Figure 11 is a plot of an example of a sleep time prediction and rate in accordance with some embodiments;

[0021] Figures 12 and 13 are plots of results of energy preservation with a dynamic timer in two configurations, respectively, in accordance with some embodiments;

[0022] Figure 14 is a flow chart illustrating example operations of a network node in accordance with some embodiments;

[0023] Figure 15 is a block diagram of a communication system in accordance with some embodiments;

[0024] Figure 16 is a block diagram of a user equipment (UE) in accordance with some embodiments;

[0025] Figure 17 is a block diagram of a network node in accordance with some embodiments; and

[0026] Figure 18 is a block diagram of a virtualization environment in accordance with some embodiments.DETAILED DESCRIPTION

[0027] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art, in which examples of embodiments of the present disclosure are shown. Inventive concepts may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art. It should also be noted that these embodiments are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present / used in another embodiment.

[0028] As discussed herein, there currently exist certain challenges. For example, while a MIMO sleep feature may have been activated in an operator network, the MIMO sleep feature may have limitations while at the same time it may not provide enough energy savings. Limitations may include, for example, a set point of the capacity on the radio unit(s) related to a PRB and the number of UEs; and a fixed timer related to the MIMO sleep function.

[0029] Technology for radio units or air units may consume a large amount of power. While a MIMO configuration and operation may help improve orchestration of the radio unit and power transmitter of transmission (Tx) to reduce the energy consumption, there may be a lack of efficient optimization techniques for reducing the energy usage during operation at a node level and sector level.

[0030] However, reducing energy consumption in MIMO operation may be a challenge. For example, how often and when to control the radio unit or branches can be difficult to predict without impacting the degradation and considering dynamic traffic variations.

[0031] Moreover, delays in controlling the radio unit can waste saved energy. Thus, there may be a need to minimize delays in the system in order to save more power in a more efficient way.

[0032] As discussed, some approaches such as a MIMO sleep mode feature may reduce power consumption in radios (also referred to herein as radio units) in a node. This may be achieved by automatically changing a MIMO configuration to a smaller MIMO or a single input multiple output (SIMO) configuration when low traffic conditions are detected in a cell. In a smaller MIMO and SIMO configuration, power consumption may be reduced either due to lower radio frequency (RF) at the radio unit, or by shutting down a radio unit power amplifier of a deactivated transmit (Tx) antenna(s).

[0033] The decision point and capability in such approaches can depend on the radio unit capacity.

[0034] Figure 1 is a schematic drawing of a high level example of a multiple (MU) MIMO arrangement. The MU MIMO 100 arrangement includes a radio 102 having multiple Tx antennas and radios 104a, 104b having multiple receive (Rx) antennas.

[0035] Enabling energy savings in an air radio that has multiple antenna branches can depend on the design of the radio architecture, for example, related to a 64 or 32 Tx arrangement and how the Tx arrangement is segmentized in a printed circuit board (PCB). The different segments may be powered from different direct current (DC) converters, or different power branches that allow the branches to turn on / off depending on the traffic conditions, for example.

[0036] Another approach may include an energy efficiency model for a MIMO system while a general and canonical system model is used for a single-cell scenario. Such an approach may use a linear processing scheme for detection and precoding, e.g., minimum mean squared error (MMSE), zero-forcing (ZF), and maximum ratio transmission (MRT / MRC); and the model may include a power dissipation model that considers overall power consumption in uplink and downlink communications. The model may also include the total power consumed by power amplifier and circuit components at a base station (BS) and single antenna UE. An optimal number of BS antennas to serve total UEs and the overall transmitted power also may be computed. Such an approach, however, may need considerable improvements in the gain of area throughput and energy efficiency by using simulation, for example.

[0037] In contrast, examples of the present disclosure consider a time for cell sleep and a plateau finding as input features. As referred to herein, the term “plateau” refers to an idle / sleep state having a static plateau / level when a radio has no traffic and / or to an active / non-sleep state having a dynamic plateau / level when a radio has traffic. There can be dynamic variance of the plateau / level depending on variation of the traffic.

[0038] Consideration of a time for cell sleep and a plateau / level finding as input features is in contrast to an approach, for example, that has an overall analysis and list of massive MIMO fifth generation (5G) radio energy consumption at different loads for qualitative energy efficiency design goals for emerging extreme MIMO systems. See e.g., S. Wesemann, J. Du, H. Viswanathen, Energy Efficient Extreme MIMO: Design Goals and Directions, arXiv:2301.01119, doi.org / 10.48550 / arXiv.2301.01119 (2023).

[0039] Examples herein also are in contrast to another approach, for example, in which energy efficiency improvement is sought through switching on / off underutilized BSs via a location-aware approach, where data from an optimal active BS set is stored in a radio environment map (REM). For efficient acquisition, processing and utilization of the REM data, reinforcement learning (RL) algorithms may be used. Exploration / exploitation methods including e-greedy, Upper Confidence Bound (UCB), and Gradient Bandit also may be used. In such an approach, the reward function may be the bit rate. See e.g., M. Hoffmann, P. Kryszkiewicz, A. Kliks, Increasing Energy Efficiency of Massive-MIMO Network via Base Stations Switching using Reinforcement Learning and Radio Environment Maps, arXiv: 2013.11891vl, doi.org / 10.48550 / arXiv.2103.11891 (2021) (Hoffinann).

[0040] Further, examples herein also are in contrast to an approach that considers and analyzes the energy efficiency of cooperative beamforming where the source and relay nodes construct a virtual multiple-input single output (MISO) beamforming system. This approach may consider all the energy consumption overheads incurred in forming a virtual MISO system; and cooperative beamforming may achieve a higher energy efficiency and better spectral efficiency for large transmission distances. The approach also may consider symbol transmission within a channel. See e.g., Hoffmann.

[0041] In contrast, for example, examples herein do not include transmission time adjustments and, instead, consider the time for cell sleep and a plateau finding as input features.

[0042] Enabling an efficient radio branch or radio unit on / off can include careful consideration to not impact the quality of service considering overlapping antenna branches as shown in the example in Figure 2. Figure 2 is a schematic drawing of antenna muting considering a control mechanism for the antennas based on PRB utilization, where different antennas are turned on / off based on fixed parameters.

[0043] In contrast to other approaches, examples herein may enable or enhance energy savings based on a better understanding and control of MIMO that includes timing and delay as factors.

[0044] Some examples include a process for orchestrating MIMO configurations to save energy in a network. The process can include a data pipeline and framework, including a data preprocessing engine. The data preprocessing engine can include MIMO configuration input data which combines configuration management (CM) and performance management (PM) counters information for MIMO sleep.

[0045] Further, some examples include a model training engine that blends three ML models: a light gradient boosting machine (lightGBM) as a base line, and a transformer ML model and a sequence transformer ML model as advanced sequence ML models to enhance the accuracy for energy prediction, e.g., on the order of the next few hours

[0046] Thus, some examples herein leverage the blending of three different ML models:(1) lightGBM, which can include a quite fast and efficient tree-based ML model. See e.g., G. Ke, Q. Meng, T, Finely, T. Wang, W. Chen, W. Ma, Q. Ye, T. Liu, LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Advances in Neural Information Processing Systems proceedings.neurips.cc / paper_files / paper / 2017 / file / 6449f44al02fde848669bdd9eb6b76fa- Paper.pdf, 30 (NIP 2017) (December 2017). lightGBM may perform well for regression and classification tasks at a scalable level with reasonable efficiency for prototyping and deployment. Some examples herein include use of lightGBM to test the quality of the data and ML model performance as a baseline, such that iteration and evaluation of the process of examples may be performed quickly to determine whether a process is reasonable or not;(2) a transformer, which can include a deep learning ML model that can learn sequential data and time series data better than regular ML models. A transformer may originate a learning sentence in natural language tasks. See e.g., A. Vaswani, N. Shazeer, N. Pamar, J. Uszoreit, L. Jones, A. Gomez, L. Kaiser, I. Polosukhin, Attention Is All You Need, proceedings.neurips.cc / paper_files / paper / 2017 / file / 3f5ee243547dee91fbd053clc4a845aa- Paper.pdf. Further, a transformer may be extended to many tasks such as time series and analysis for long sequences. Examples herein may include data that is based on energy consumption on hourly basis, which is a timeseries data; and a transformer ML model(s) may be applied to enhance the accuracy of the energy prediction on top of a base ML model; and / or(3) a sequence transformer, is a different ML model from a base transformer ML model and may be enhanced for time series and sequence data. A sequence transformer ML model is applied in some examples together with a base lightGBM ML model and a base transformer ML model so that a final prediction for energy consumption reaches a high accuracy score.

[0047] Some examples also include a prediction engine and decision-making engine implementing the decision to achieve the predicted energy consumption.

[0048] Examples herein include use of ML to reduce energy consumption by scaling a radio unit in MIMO operation, based on considering time of use, and delay of radio unit activation and deactivation.

[0049] As discussed, some examples herein further include a data loading pipeline for MIMO configuration; a MIMO tum-on / off strategy engine based on accurate prediction from ML models including time shift; and / or a dynamic timer for a tum-on / off switch using a plateau finding.

[0050] figure 3 is a schematic drawing of a general framework for a MIMO sleep control strategy in accordance with some embodiments. The framework includes ML operations that may orchestrate a MIMO radio to enable energy savings.

[0051] As shown in the example in Figure 3, a data preprocessing engine 306 receives or accesses MIMO configuration input data 300 which combines CM and PM counters information for MIMO sleep of a MIMO Tx radio 302 and / or a MIMO Rx radio 304. A model training engine 308 can include three ML models as discussed herein. For example, the model training engine 308 can include lightGBM as a base line ML model, and a transformer ML model and a sequence transformer ML model as advanced sequence learning models to enhance the accuracy for energy prediction in a time period such as next few hours.

[0052] A prediction engine 310 for future energy consumption can also be included. Further, a decision making engine 312 can be included that implements how the predicted energy consumption can be used to turn on-off radio configurations in order to save energy consumption in an upcoming time period, such as upcoming hours, and set a timer for turn on / off.

[0053] In some examples, considering the traffic profiles of different nodes, including different sectors / bands / radios, and not only the time of use of a radio unit (that is, when the radio is active) but also the sleeping duration is important.

[0054] Different MIMO radio bands may handle different radio traffic profiles / pattems that may need to be considered during a time window such as for 24 hours.

[0055] In some examples, minimizing the sleeping time duration per radio unit or per branch is important, as well consideration of an impact on energy saving in the radio unit of delay time associated with taking a decision to turn on / off a radio.

[0056] Figure 4 is a schematic drawing of an example MIMO site configuration in accordance with some embodiments. As shown in this example, the site has a station identifier (ID) and includes three sectors 1-3. In this example, there are three radio units per sector where each radiounit has a radio ID: Sector 1 includes radio units 1-3 having radio IDs 1, 2, 3, respectively; Sector 2 includes radio units 4-6 having radio IDS 4, 5, 6, respectively; and Sector 3 includes radio units 7-9 having radio IDs 7, 8, 9, respectively. Different bands are considered on different sectors in this deployment, based on the capacity requirement on the specific site or location.

[0057] It is understood that Figure 4 is a non-limiting example and there can be different radio configuration deployments having different energy usage.

[0058] In the example in Figure 4, the radio units 1-9 have different Tx / Rx configurations that can be controlled separately, which is visualized by the arrows in Figure 4.

[0059] Data and MIMO-related counters of some examples is now discussed further. Figure 5 is a schematic drawing of an overall data set and tables from real-time MIMO configuration files. The MIMO configuration data can be preprocessed and formatted, and can include the example features shown in Figure 5:• Cell data 500, which can include a station ID, time stamp, cell frequency division duplex (FDD), PRB consumed, etc.• Energy data 502, which can include a station ID, a radio ID, consumed energy, time stamp, etc.• RF port data 504, which can include a radio ID, time stamp, number of active ports, sector ID, etc.• MIMO sleep data 506, which in this example is data filtered from the data 500, 502, 504 and includes the station ID, the time stamp, and the cell FDD.

[0060] A process can leverage rich features from the real-time MIMO configuration file from multiple hours within multiple days, for example. The following Table 1 is an example of an excerpt of input features from the MIMO configuration data in Figure 5. The example input features correspond to Figure 4 and include station ID; sector ID, where the station contains 3 sectors; radio ID, where each sector contains three radios; hour, which is a timestamp that show the hour a radio activates; pmPrbUsedDlSum, which is the total PRB utilization currently; and pmConsumerEnergy, which is a target variable and is the total energy consumed based on a current configuration:

[0061] The overall input data in this example includes a time-series dataset marked by hours per day for each given radio ID, sector ID, and cell ID for the configuration shown in Figure 4. To optimize, e.g., minimize, the overall energy consumption, the process determines a MIMO turn on / off strategy based on a sector and radio level and predicted energy consumption for the next hours. For example, for station ID 1, the data can be sorted by ascending time such that the consumed energy is atime-series target depending on PRB used (e.g., pmPrbUsedDLSum) at each time stamp. The other features categorize the station, radio and sector, which can be easy to process. For all categorical features, regular label encoding techniques can be applied.

[0062] A goal in this example is to optimize the total consumption of the energy for each sector of the three sectors by turning on / off radios (e.g., 1 or 2 radios) and replacing the configuration with similar PRB utilization based on the prediction of the ML models and a smart search from historical settings.

[0063] Figure 6 is a schematic drawing of an overall structure and process for this example. As shown, the structure includes (1) a MIMO data loader 602 which combines important features together; (2) an optimizer 608, such as a mini linear regressor, that identifies a better energy based on the prediction 606 from the ML model(s) 604, and uses smart thresholding to find the optimal PRB; and (3) a decision-making engine 610 that takes the results from the optimizer 608 and decides a MIMO strategy that includes which sector should be switched on 612 to replace an existing sector. The decision-making engine 610 also computes a plateau / level of each configuration in terms of the dynamic decision-making process and, therefore, a dynamic timer adjustment can be applied to each configuration rather than repeated / constant switch on / off timing used in other approaches.

[0064] In some examples, the estimator 606 includes ML models trained using a regular ML model (e.g., lightGBM) and a deep learning model(s) (e.g., atransformer ML model(s)). The target variable (e.g., consumed energy) is shifted in time by t+1, t+2, . . . as the ML models predict the target variable for x-hour forward. The pretrained ML model(s) 604 takes new input data from MIMO data loader 602 as streaming data and predict the energy consumed for t+1, t+2, . . . given the features (e.g., configurations) at time t.

[0065] Once the future energy consumed is predicted with reasonable accuracy as discussed further herein, the optimizer 608 decides if one radio needs to be turned off by using a better configuration, which is based on an optimization, such as mini liner regressor learned from historical data.

[0066] Figure 7 is a schematic drawing of an example of operations of the optimizer 608 ofFigure 6 for determining optimized energy. Based on the prediction from the combined ML models, a search is performed to identify a similar PrbUsed from historical data (shown in Figure7 as Consumed Energy) with the current PrbUsed (shown in Figure 7 as Consumed Energy), but which consumes a smaller amount of energy. As shown in the plot in Figure 7 for this example, there is a linear relationship from some historical data between PrbUsed and the consumed energy.Optimizer 608 can be based on this finding. As shown, if the similar PRB consumes less energy than the current configuration (that is, similar PRB with high energy consumption from the prediction), then in 610a the present configuration can be switched off and the configuration with less energy consumption can be turned on; otherwise, in 610b, the current configuration is kept.The similarity can be computed as shown in Figure 7 using a threshold: currentPrb-historical Prb historical Prb < threshold.

[0067] To find an improved optimal configuration, it may be important to predict the energy consumption for a future time period such as the next hours. Evaluation of performance of combined ML models for an example is now discussed. The evaluation shows that use the combined ML models in this example resulted in high accuracy based on mean-squared-error between predicted energy and ground truth in a test dataset.

[0068] Figure 8 is a plot from the evaluation of a prediction of consumed energy for 500 samples for a next hour. That is, Figure 8 compares the combined ML models’ performance for predicted energy in the next hour with ground truth in the test dataset. As shown, the combined ML models showed high accuracy in terms of mean squared error; and the prediction generally follows the ground truth values related to energy consumption.

[0069] Figure 9 is a plot from the evaluation of an R2 score used to measure how well the combined ML models’ prediction matches the ground truth for consumed energy. As shown, the R2 score in this example is 0.98111 which shows the combined ML models gave a high accuracy of energy prediction in this example.

[0070] Another example includes finding a static power level and a dynamic power level that is, e.g., a power level within a different PRB. In this example, a level of static power per RF port is found where the power of the RF port is low for a PRB. Next, a dynamic power level is foundbased on linear PRB values. A goal of this example is an improved turn on / off of a radio that allows the radio to be set to sleep for more time. Finding the levels where the radio(s) / RF port(s) is staying on and for how long in time is used to find the static level. Figure 10 is a plot showing an example of a static variance and dynamic variance.

[0071] Sleeping time control, including a rate change prediction and control, is now discussed.

[0072] In an example, turning a MIMO radio on / off 1100 is evaluated based on including an increase of sleeping time. Figure 11 is a plot of an example of a sleep time prediction and rate. As shown, unlike other approaches where a sleep time is set, e.g., 30 minutes per hour; in this example, the process dynamically adjusts a sleep timer based on the optimizer 608 and the decision making engine 610. As shown, a radio has a first MIMO sleep off period 1102 where the radio is on and MIMO sleep is off. Based on a prediction of the combined ML models, a switch down monitor duration timer (shown as switchDownMonitorDurTimer) is activated that turns off an RF port of the radio for a duration 1104. A switch up monitor duration timer (shown as switchUpMonitorDurTimer) is activated that turns on the RF port of the radio for another duration 1106, and so on. The timers are activated in this example when a switch down PRB utilization threshold value and a switch down radio resource control (RRC) connection threshold (e.g., a number of UE) or a switch up PRB utilization threshold value and a switch up RRC connection threshold (e.g., a number of UE) are reached. Thus, the prediction(s) overcome sleep timer settings, such as for fixed time periods, of other approaches. That is, in this example, the process dynamically adjusts the sleep timing based on the optimizer 608 and the decision-making engine 610.

[0073] In another example, a timer setting in an ML model can be elaborated via the PRB variations. The PRB dynamicity can set the new timer setting of the operation for each radio unit in Figure 4, for example. The new timer setting can dynamically change and be applied to the system.

[0074] Results of energy preservation with a dynamic timer in two configurations is now discussed with respect to Figures 12 and 13. As shown in Figure 12 by the timing plots, an example process herein dynamically set the timer instead of constant timing of other approaches. Moreover, as shown by the plot in Figure 12, there was a savings of up to 3% of energy consumption before and after the process of the example using the dynamic timer for radio 1 in sector 1 in Figure 4. The energy saving was calculated by the ratio of the sum of total energy consumption before and after applying the process for each configuration. In Figure 12, the dynamic timing was assigned according to the decision-making engine 610 that switched on / off the radio based on thecomputation by the optimizer 608. The previous configuration had set a constant timing of MIMO sleeping, whereas the process of this example including dynamic time adjustment helped to save energy.

[0075] In the plot in Figure 12, the line having the larger circles shows the result of the example process where the timer was dynamically set; and the line having the smaller circles shows the result of the previous configuration that had a constant timing if MIMO sleeping.

[0076] In some examples, the turn on / off timer is optimized dynamically instead of having a constant timer pulse.

[0077] Figure 13 shows results of energy preservation with a dynamic timer from another configuration. As shown in Figure 13 by the timing plots, an example process herein dynamically set the timer instead of former constant timing of other approaches. Moreover, as shown by the plot in Figure 13, there was a savings of up to 12% of energy consumption before and after the process of the example using the dynamic timer for radio 2 in sector 2 in Figure 4. The energy saving was calculated by the ratio of the sum of total energy consumption before and after applying the process for each configuration. In Figure 13, the dynamic timing was assigned according to the decision-making engine 610 that switched on / off the radio based on the computation by the optimizer 608. The previous configuration had set a constant timing of MIMO sleeping, whereas the process of this example including dynamic time adjustment helped to save energy.

[0078] In the plot in Figure 13, the line having the larger circles shows the result of the example process where the timer was dynamically set; and the line having the smaller circles shows the result of the previous configuration that had a constant timing of MIMO sleeping.

[0079] Figure 14 is a flow chart of operations of a network node in accordance with some embodiments.

[0080] Referring to Figure 14, in some embodiments, a computer-implemented method is provided that is performed by a network node for orchestrating MIMO operation of a plurality of radios in a communications system. The method includes accessing 1400 data including historical MIMO configuration data for the plurality of radios. The operations further include predicting 1404 for a future time period, with at least one first ML model, an estimated energy consumption for a first PRB utilization of a first radio in a first sector. The operations further include predicting 1406, with a second ML model based on the estimated energy consumption of the first radio, a historical energy consumption for a second PRB utilization of a second radio in a second sector. The operations further include deciding 1412 one of (i) to turn off a first MIMO configuration of the first radio and to turn on a second MIMO configuration of the second radio when the historicalenergy consumption is less than or equal to the estimated energy consumption, and (ii) to keep the first MIMO configuration of the first radio when the historical energy consumption is greater than the estimated energy consumption.

[0081] In some embodiments, the method further includes finding 1408 (i) a first level of consumed energy of the first MIMO configuration where the first radio is on for a first time period and (ii) a second level of consumed energy of the second MIMO configuration where the second radio is on for a second time period, respectively; and applying 1414 a timer adjustment to at least one of (i) turn on or off the first MIMO configuration of the first radio based on the first level and (ii) turn on or off the second MIMO configuration of the second radio based on the second level.

[0082] The historical MIMO configuration data can include configuration management counters information and performance management information.

[0083] In some embodiments, the historical MIMO configuration data includes at least one of (i) a station identifier, (ii) a sector identifier for respective sectors, (iii) a radio identifier of the plurality of radios, (iv) an indication of a time a radio from the plurality of radios was activated, (v) a total physical resource block, PRB, utilization, and (vi) a total energy consumed based on the first MIMO configuration of the first radio.

[0084] In some embodiments, the at least one first ML model includes three ML models including a light gradient boosting machine, a transformer model, and a sequence transformer model.

[0085] The second ML model can include a linear regression model that estimates a mapping between PRB and consumed energy from the historical MIMO configuration data.

[0086] The second ML model can search the historical MIMO configuration data to predict the second PRB utilization that uses less energy consumption than the estimated energy consumption for the first PRB utilization.

[0087] The historical MIMO configuration data can include historical PRB utilization and historical energy consumed and at least one of the first and second PRB utilization has a linear relationship with the consumed energy.

[0088] In some embodiments, the first PRB utilization less the second PRB utilization, divided by the second PRB utilization, is less than or equal to a threshold value.

[0089] In other embodiments, the method further includes training 1402 the at least one ML model based on, for a future time period, shifting the estimated energy consumption at a plurality of time increments from an initial time.

[0090] Predicting 1404 can include predicting the estimated energy consumption for respective time increments in the future time period given the historical MIMO configuration data at the initial time.

[0091] The first level can correspond to a first static level of consumed power per RF port of the first radio and the second level corresponds to a second static level of consumed power per RF port of the second radio.

[0092] In some embodiments, the method further includes finding 1410 (i) variations in consumed energy of the first MIMO configuration corresponding to variations in PRB utilization of the first radio and (ii) variations in consumed energy of the second MIMO configuration corresponding to variations in PRB utilization of the second radio.

[0093] Applying 1410 the timer adjustment can include increasing a sleep time duration of at least one of the first radio and the second radio.

[0094] The timer adjustment can be based on variations in PRB utilization.

[0095] In some embodiments, the network node includes at least one of a network node and a cloud-based network node.

[0096] Operations of a network node can be performed by the network node 1700 of Figure 17. For example, modules may be stored in memory 1704 of Figure 17 and these modules may provide instructions so that when the instructions of a module are executed by respective network node processing circuitry 1702, network node 1700 performs respective operations of the flow chart.

[0097] Various operations from the flow chart of Figure 14 may be optional with respect to some embodiments of network node and related methods. For example the operations of blocks 1402, 1408, 1410, and 1414 may be optional.

[0098] Figure 15 shows an example of a communication system 1500 in accordance with some embodiments.

[0099] In the example, the communication system 1500 (also referred to herein as a network) includes a telecommunication network 1502 that includes an access network 1504, such as a RAN, and a core network 1506, which includes one or more core network nodes 1508. The access network 1504 includes one or more access network nodes, such as network nodes 1510a and 1510b (one or more of which may be generally referred to as network nodes 1510), or any other similar 3GPP access node or non-3GPP access point. The network nodes 1510 facilitate direct or indirect connection of UE, such as by connecting UEs 1512a, 1512b, 1512c, and 1512d (one or more of which may be generally referred to as UEs 1512) to the core network 1506 over one or morewireless connections. A network node for orchestrating MIMO operation of a plurality of radios (e.g., network node 1510, 1518, 1700) can be an access network node 1510, a core network node 1508, a cloud-based network node 1518, or another node for orchestrating MIMO operation of a plurality of radios in communication system 1500.

[0100] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 1500 may include any number of wired or wireless networks, network nodes, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 1500 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0101] The UEs 1512 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 1510 and other communication devices. Similarly, the network nodes 1510 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 1512 and / or with other network nodes or equipment in the telecommunication network 1502 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 1502.

[0102] In the depicted example, the core network 1506 connects the network nodes 1510 to one or more hosts, such as host 1516. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 1506 includes one more core network nodes (e.g., core network node 1508) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 1508. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0103] The host 1516 may be under the ownership or control of a service provider other than an operator or provider of the access network 1504 and / or the telecommunication network 1502, and may be operated by the service provider or on behalf of the service provider. The host 1516 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0104] As a whole, the communication system 1500 of Figure 15 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0105] In some examples, the telecommunication network 1502 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 1502 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1502. For example, the telecommunications network 1502 may provide URLLC services to some UEs, while providing eMBB services to other UEs, and / or mMTC / Massive loT services to yet further UEs.

[0106] In some examples, the UEs 1512 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1504 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1504. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0107] In the example, the hub 1514 communicates with the access network 1504 to facilitate indirect communication between one or more UEs (e.g., UE 1512c and / or 1512d) and network nodes (e.g., network node 1510b). In some examples, the hub 1514 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1514 may be a broadband router enabling access to the core network 1506 for the UEs. As another example, the hub 1514 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 1510, or by executable code, script, process, or other instructions in the hub 1514. As another example, the hub 1514 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 1514 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1514 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1514 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1514 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0108] The hub 1514 may have a constant / persistent or intermittent connection to the network node 1510b. The hub 1514 may also allow for a different communication scheme and / or schedule between the hub 1514 and UEs (e.g., UE 1512c and / or 1512d), and between the hub 1514 and the core network 1506. In other examples, the hub 1514 is connected to the core network 1506 and / or one or more UEs via a wired connection. Moreover, the hub 1514 may be configured to connect to an M2M service provider over the access network 1504 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1510 while still connected via the hub 1514 via a wired or wireless connection. In some embodiments, the hub 1514 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 1510b. In other embodiments, the hub 1514 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1510b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0109] Figure 16 shows a UE 1600 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smartphone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0110] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X) . In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).[oni] The UE 1600 includes processing circuitry 1602 that is operatively coupled via a bus 1604 to an input / output interface 1606, a power source 1608, a memory 1610, a communication interface 1612, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 16. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0112] The processing circuitry 1602 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 1610. The processing circuitry 1602 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1602 may include multiple central processing units (CPUs).

[0113] In the example, the input / output interface 1606 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 1600. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, atrackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0114] In some embodiments, the power source 1608 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 1608 may further include power circuitry for delivering power from the power source 1608 itself, and / or an external power source, to the various parts of the UE 1600 via input circuitry or an interface such as an electrical power cable . Delivering power may be, for example, for charging of the power source 1608. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1608 to make the power suitable for the respective components of the UE 1600 to which power is supplied.

[0115] The memory 1610 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1610 includes one or more application programs 1614, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1616. The memory 1610 may store, for use by the UE 1600, any of a variety of various operating systems or combinations of operating systems.

[0116] The memory 1610 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) opticaldisc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1610 may allow the UE 1600 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1610, which may be or comprise a device-readable storage medium.

[0117] The processing circuitry 1602 may be configured to communicate with an access network or other network using the communication interface 1612. The communication interface 1612 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1622. The communication interface 1612 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 1618 and / or a receiver 1620 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1618 and receiver 1620 may be coupled to one or more antennas (e.g., antenna 1622) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0118] In the illustrated embodiment, communication functions of the communication interface 1612 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802. 11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0119] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1612, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0120] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0121] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city, wearable technology, extended industrial application, and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 1600 shown in Figure 16.

[0122] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As oneparticular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0123] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0124] Figure 17 shows a network node 1700 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, network nodes, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)), 0-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0125] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an 0-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0126] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes,Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0127] The network node 1700 includes a processing circuitry 1702, a memory 1704, a communication interface 1706, and a power source 1708. The network node 1700 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 1700 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1700 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 1704 for different RATs) and some components may be reused (e.g., a same antenna 1710 may be shared by different RATs). The network node 1700 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1700, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1700.

[0128] The processing circuitry 1702 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable network node, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 1700 components, such as the memory 1704, to provide network node 1700 functionality.

[0129] In some embodiments, the processing circuitry 1702 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1702 includes one or more of RF transceiver circuitry 1712 and baseband processing circuitry 1714. In some embodiments, the RF transceiver circuitry 1712 and the baseband processing circuitry 1714 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1712 and baseband processing circuitry 1714 may be on the same chip or set of chips, boards, or units.

[0130] The memory 1704 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device -readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1702. The memory 1704 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 1702 and utilized by the network node 1700. The memory 1704 may be used to store any calculations made by the processing circuitry 1702 and / or any data received via the communication interface 1706. In some embodiments, the processing circuitry 1702 and memory 1704 is integrated.

[0131] The communication interface 1706 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 1706 comprises port(s) / terminal(s) 1716 to send and receive data, for example to and from a network over a wired connection. The communication interface 1706 also includes radio front-end circuitry 1718 that may be coupled to, or in certain embodiments a part of, the antenna 1710. Radio front-end circuitry 1718 comprises filters 1720 and amplifiers 1722. The radio front-end circuitry 1718 may be connected to an antenna 1710 and processing circuitry 1702. The radio front-end circuitry may be configured to condition signals communicated between antenna 1710 and processing circuitry 1702. The radio front-end circuitry 1718 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio frontend circuitry 1718 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1720 and / or amplifiers 1722. The radio signal may then be transmitted via the antenna 1710. Similarly, when receiving data, the antenna 1710 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1718. The digital data may be passed to the processing circuitry 1702. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0132] In certain alternative embodiments, the network node 1700 does not include separate radio front-end circuitry 1718, instead, the processing circuitry 1702 includes radio front-end circuitry and is connected to the antenna 1710. Similarly, in some embodiments, all or some of theRF transceiver circuitry 1712 is part of the communication interface 1706. In still other embodiments, the communication interface 1706 includes one or more ports or terminals 1716, the radio front-end circuitry 1718, and the RF transceiver circuitry 1712, as part of a radio unit (not shown), and the communication interface 1706 communicates with the baseband processing circuitry 1714, which is part of a digital unit (not shown).

[0133] The antenna 1710 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1710 may be coupled to the radio front-end circuitry 1718 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1710 is separate from the network node 1700 and connectable to the network node 1700 through an interface or port.

[0134] The antenna 1710, communication interface 1706, and / or the processing circuitry 1702 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 1710, the communication interface 1706, and / or the processing circuitry 1702 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0135] The power source 1708 provides power to the various components of network node 1700 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1708 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1700 with power for performing the functionality described herein. For example, the network node 1700 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1708. As a further example, the power source 1708 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0136] Embodiments of the network node 1700 may include additional components beyond those shown in Figure 17 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 1700 may include userinterface equipment to allow input of information into the network node 1700 and to allow output of information from the network node 1700. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1700.

[0137] Figure 18 is a block diagram illustrating a virtualization environment 1800 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1800 hosted by one or more of hardware nodes, such as a hardware network node that operates as a network node, network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1800 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a SMO 1810 Framework via an O-2 interface.

[0138] Applications 1802 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1800 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0139] Hardware 1804 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1806 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1808a and 1808b (one or more of which may be generally referred to as VMs 1808), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1806 may present a virtual operating platform that appears like networking hardware to the VMs 1808.

[0140] The VMs 1808 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1806. Different embodiments of the instance of a virtual appliance 1802 may be implemented on one ormore of VMs 1808, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0141] In the context of NFV, a VM 1808 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine . Each of the VMs 1808, and that part of hardware 1804 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1808 on top of the hardware 1804 and corresponds to the application 1802.

[0142] Hardware 1804 may be implemented in a standalone network node with generic or specific components. Hardware 1804 may implement some functions via virtualization. Alternatively, hardware 1804 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration (SMO) 1810, which, among others, oversees lifecycle management of applications 1802. In some embodiments, hardware 1804 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1812 which may alternatively be used for communication between hardware nodes and radio units.

[0143] Although the network node described herein may include the illustrated combination of hardware components, other embodiments may comprise network nodes with different combinations of components. It is to be understood that these network nodes may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result ofsaid processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, network nodes may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processor and the network interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0144] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the network node, but are enjoyed by the network node as a whole, and / or by end users and a wireless network generally.

[0145] In certain embodiments, a network node (1510, 1518, 1700) is provided. The network node is configured to orchestrate MIMO operation of a plurality of radios in a communications system. The network node includes processing circuitry 1718; and memory 1704 coupled with the processing circuitry. The memory includes instructions that when executed by the processing circuitry causes the network node to perform operations. The operations include to perform some or all of the functionality described herein.

[0146] In certain embodiments, a non-transitory computer readable medium 1704 including program code to be executed by processing circuitry 1702 of a network node 1510, 1518, 1700 is configured to orchestrate MIMO operation of a plurality of radios in a communications system. Execution of the program code causes the program code to perform operations. The operations include to perform some or all of the functionality described herein.

[0147] Further definitions and embodiments are discussed below.

[0148] In the above-description of certain embodiments of the present disclosure, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which concepts of the present disclosure belong. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0149] When an element is referred to as being “connected”, “coupled”, “responsive”, or variants thereof to another element, it can be directly connected, coupled, or responsive to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected”, “directly coupled”, “directly responsive”, or variants thereof to another element, there are no intervening elements present. Like numbers refer to like elements throughout. Furthermore, “coupled”, “connected”, “responsive”, or variants thereof as used herein may include wirelessly coupled, connected, or responsive. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise . Well-known functions or constructions may not be described in detail for brevity and / or clarity. The term “and / or” (abbreviated “ / ”) includes any and all combinations of one or more of the associated listed items.

[0150] It will be understood that although the terms first, second, third, etc. may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another element / operation. Thus a first element / operation in some embodiments could be termed a second element / operation in other embodiments without departing from the teachings of concepts of the present disclosure. The same reference numerals or the same reference designators denote the same or similar elements throughout the specification.

[0151] As used herein, the terms “comprise”, “comprising”, “comprises”, “include”, “including”, “includes”, “have”, “has”, “having”, or variants thereof are open-ended, and include one or more stated features, integers, elements, steps, components, or functions but does not preclude the presence or addition of one or more other features, integers, elements, steps, components, functions, or groups thereof. Furthermore, as used herein, the common abbreviation “e.g.”, which derives from the Latin phrase “exempli gratia,” may be used to introduce or specifya general example or examples of a previously mentioned item, and is not intended to be limiting of such item. The common abbreviation “i.e ”, which derives from the Latin phrase “id est,” may be used to specify a particular item from a more general recitation.

[0152] Example embodiments are described herein with reference to block diagrams and / or flowchart illustrations of computer-implemented methods, apparatus (systems and / or devices) and / or computer program products. It is understood that a block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by computer program instructions that are performed by one or more computer circuits. These computer program instructions may be provided to a processor circuit of a general purpose computer circuit, special purpose computer circuit, and / or other programmable data processing circuit to produce a machine, such that the instructions, which execute via the processor of the computer and / or other programmable data processing apparatus, transform and control transistors, values stored in memory locations, and other hardware components within such circuitry to implement the functions / acts specified in the block diagrams and / or flowchart block or blocks, and thereby create means (functionality) and / or structure for implementing the functions / acts specified in the block diagrams and / or flowchart block(s).

[0153] These computer program instructions may also be stored in a tangible computer- readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instructions which implement the functions / acts specified in the block diagrams and / or flowchart block or blocks. Accordingly, embodiments of the present disclosure may be embodied in hardware and / or in software (including firmware, resident software, micro-code, etc.) that runs on a processor such as a digital signal processor, which may collectively be referred to as “circuitry,” “a module” or variants thereof.

[0154] It should also be noted that in some alternate implementations, the functions / acts noted in the blocks may occur out of the order noted in the flowcharts. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality / acts involved. Moreover, the functionality of a given block of the flowcharts and / or block diagrams may be separated into multiple blocks and / or the functionality of two or more blocks of the flowcharts and / or block diagrams may be at least partially integrated. Finally, other blocks may be added / inserted between the blocks that are illustrated, and / or blocks / operations may be omitted without departing from the scope of the present disclosure. Moreover, although some of the diagrams include arrows oncommunication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

[0155] Many variations and modifications can be made to the embodiments without substantially departing from the principles of the present disclosure. All such variations and modifications are intended to be included herein within the scope of present disclosure.Accordingly, the above disclosed subject matter is to be considered illustrative, and not restrictive, and the examples of embodiments are intended to cover all such modifications, enhancements, and other embodiments, which fall within the spirit and scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the present disclosure including the examples of embodiments and their equivalents, and shall not be restricted or limited by the foregoing detailed description.

Claims

Claims:

1. A computer-implemented method performed by a network node for orchestrating multiple input multiple output, MIMO, operation of a plurality of radios in a communications system, the method comprising: accessing (1400) data comprising historical MIMO configuration data for the plurality of radios; predicting (1404) for a future time period, with at least one first machine learning, ML, model, an estimated energy consumption for a first physical resource block, PRB, utilization of a first radio in a first sector; predicting (1406), with a second ML model based on the estimated energy consumption of the first radio, a historical energy consumption for a second PRB utilization of a second radio in a second sector; and deciding (1412) one of (i) to turn off a first MIMO configuration of the first radio and to turn on a second MIMO configuration of the second radio when the historical energy consumption is less than or equal to the estimated energy consumption, and (ii) to keep the first MIMO configuration of the first radio when the historical energy consumption is greater than the estimated energy consumption.

2. The method of Claim 1, further comprising: finding (1408) (i) a first level of consumed energy of the first MIMO configuration where the first radio is on for a first time period and (ii) a second level of consumed energy of the second MIMO configuration where the second radio is on for a second time period, respectively; and applying ( 1414) a timer adjustment to at least one of (i) turn on or off the first MIMO configuration of the first radio based on the first level and (ii) turn on or off the second MIMO configuration of the second radio based on the second level.

3. The method of any one of Claims 1 to 2, wherein the historical MIMO configuration data comprises configuration management counters information and performance management information.

4. The method of any one of Claim 3, wherein the historical MIMO configuration datacomprises at least one of (i) a station identifier, (ii) a sector identifier for respective sectors, (iii) a radio identifier of the plurality of radios, (iv) an indication of a time a radio from the plurality of radios was activated, (v) a total physical resource block, PRB, utilization, and (vi) a total energy consumed based on the first MIMO configuration of the first radio.

5. The method of any one of Claims 1 to 4, wherein the at least one first ML model comprises three ML models comprising a light gradient boosting machine, a transformer model, and a sequence transformer model.

6. The method of any one of Claims 1 to 5, wherein the second ML model comprises a linear regression model that estimates a mapping between PRB and consumed energy from the historical MIMO configuration data.

7. The method of any one of Claims 1 to 6, wherein the second ML model searches the historical MIMO configuration data to predict the second PRB utilization that uses less energy consumption than the estimated energy consumption for the first PRB utilization.

8. The method of any one of Claims 1 to 7, wherein the historical MIMO configuration data comprises historical PRB utilization and historical energy consumed and at least one of the first and second PRB utilization has a linear relationship with the consumed energy.

9. The method of any one of Claims 1 to 8, wherein the first PRB utilization less the second PRB utilization, divided by the second PRB utilization, is less than or equal to a threshold value.

10. The method of any one of Claims 1 to 9, the method further comprising: training (1402) the at least one ML model based on, for a future time period, shifting the estimated energy consumption at a plurality of time increments from an initial time.

11. The method of Claim 10, wherein the predicting (1404) comprises predicting the estimated energy consumption for respective time increments in the future time period given the historical MIMO configuration data at the initial time.

12. The method of any one of Claims 2 to 11, wherein the first level corresponds to a first static level of consumed power per radio frequency, RF, port of the first radio and the second level corresponds to a second static level of consumed power per RF port of the second radio.

13. The method of any one of Claims 2 to 12, further comprising: finding (1410) (i) variations in consumed energy of the first MIMO configuration corresponding to variations in PRB utilization of the first radio and (ii) variations in consumed energy of the second MIMO configuration corresponding to variations in PRB utilization of the second radio.

14. The method of any one of Claims 2 to 13, wherein the applying (1410) the timer adjustment comprises increasing a sleep time duration of at least one of the first radio and the second radio.

15. The method of any one of Claims 2 to 14, wherein the timer adjustment is based on variations in PRB utilization.

16. The method of any one of Claims 2 to 15, wherein the network node comprises at least one of a network node and a cloud-based network node.

17. A network node (1510, 1518, 1700) configured to orchestrate multiple input multiple output, MIMO, operation of a plurality of radios in a communications system, the network node comprising: processing circuitry (1718); memory (1704) coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform operations comprising: access data comprising historical MIMO configuration data for the plurality of radios; predict for a future time period, with at least one first machine learning, ML, model, an estimated energy consumption for a first physical resource block, PRB, utilization of a first radio in a first sector; predict, with a second ML model based on the estimated energy consumption of the firstradio, a historical energy consumption for a second PRB utilization of a second radio in a second sector; and decide one of (i) to turn off a first MIMO configuration of the first radio and to turn on a second MIMO configuration of the second radio when the historical energy consumption is less than or equal to the estimated energy consumption, and (ii) to keep the first MIMO configuration of the first radio when the historical energy consumption is greater than the estimated energy consumption.

18. The network node of Claim 17, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform operations that further comprise any one of the operations of Claims 2 to 16.

19. A non-transitory computer readable medium (1704) including program code to be executed by processing circuitry (1702) of a network node (1510, 1518, 1700) configured to orchestrate multiple input multiple output, MIMO, operation of a plurality of radios in a communications system, whereby execution of the program code causes the program code to perform operations comprising: access data comprising historical MIMO configuration data for the plurality of radios; predict for a future time period, with at least one first machine learning, ML, model, an estimated energy consumption for a first physical resource block, PRB, utilization of a first radio in a first sector; predict, with a second ML model based on the estimated energy consumption of the first radio, a historical energy consumption for a second PRB utilization of a second radio in a second sector; and decide one of (i) to turn off a first MIMO configuration of the first radio and to turn on a second MIMO configuration of the second radio when the historical energy consumption is less than or equal to the estimated energy consumption, and (ii) to keep the first MIMO configuration of the first radio when the historical energy consumption is greater than the estimated energy consumption.

20. The non-transitory computer readable medium of Claim 19, the operations further comprising any of the operations of Claims 2-16.

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