Abstract: A method can comprise determining, by a system, a first sleep mode action for a first group of cellular base station signal metrics for a cellular base station over a first time period, wherein the first group of cellular base station signal metrics is determined based on a first layer of communications of the cellular base station. The method can further comprise determining, by the system, a second sleep mode action for a second group of cellular base station signal metrics for the cellular base station over a second time period. The method can further comprise determining, by the system, an arbitrated sleep mode action based on the first sleep mode action and the second sleep mode action. The method can further comprise sleeping, by the system, at least part of the cellular base station based on the arbitrated sleep mode action.

Abstract: The technology described herein is directed towards accurate low-complexity decision tree-based traffic predictor models, such as decision tree regressor models for use by base stations. Each model is rapidly retrained per base station using data relevant to the base station. To improve accuracy, statistically significant feature data is selected by performing hypothesis testing on candidate features to filter out features that cannot satisfy a statistical significance threshold (e.g., p-value). The decision tree regressor model is recursively grown based on the selected features' feature values and their traffic level labels. Predicted traffic level data is determined by traversing the trained decision tree to reach a leaf node associated with the prediction data. Resource allocation can be based on the prediction. In addition to time-series training data, spatial training data can be used. Real time traffic monitoring by a radio unit for operating in an autonomous management mode is also facilitated.

Abstract: Use of a data-driven approach that assimilates historical signal to interference-plus noise ratio (SINR) and channel estimation data along with location-map of the cell in which base station equipment is situated to better define the relationship between SINR and the user-channel environmental map and spatio-temporal changes to it to achieve more granular, cell site-specific modeling is disclosed herein. This data-driven approach estimates SINR using variational autoencoders. Variational encoders typically consist of two sections, an encoder section and decoder section. The encoder section learns the distribution on the low-dimensional latent space over the input data samples. The decoder section is a generative model that learns the joint distribution of the latent variables and input data.

Abstract: Use of a data-driven approach that assimilates historical signal to interference-plus noise ratio (SINR) and channel estimation data along with location-map of the cell in which base station equipment is situated to better define the relationship between SINR and the user-channel environmental map and spatio-temporal changes to it to achieve more granular, cell site-specific modeling is disclosed herein. This data-driven approach estimates SINR using variational autoencoders. Variational encoders typically consist of two sections, an encoder section and decoder section. The encoder section learns the distribution on the low-dimensional latent space over the input data samples. The decoder section is a generative model that learns the joint distribution of the latent variables and input data.

Abstract: Facilitating cell and carrier switch off for energy awareness in advanced communication networks is provided herein. A method includes determining a first result of a utility function associated with a first configuration of a set of carriers that service a group of user equipment in a communication network. The first configuration is based on respective activation states of carriers of the set of carriers. The method also includes, based on respective traffic patterns of user equipment of the group of user equipment, evaluating respective results of the utility function for respective configurations of a group of configurations, other than the first configuration, for the set of carriers. Further, the method includes selecting a second configuration from the group of configurations based on a second result of the utility function for the second configuration being determined to be a higher value than a value of the first result.

Abstract: A system can produce a first graph that represents components of a broadband cellular network, wherein the first graph comprises first nodes and first edges. The system can process the first graph with a graph neural network to produce a feature embedding matrix. The system can pool information of nodes of the first graph based on the feature embedding matrix, to produce a second graph, wherein the second graph comprises second nodes and second edges. The system can adjust a parameter of a network controller of the broadband cellular network based on the second graph.

Abstract: Facilitating Multiple-Tenant Energy Efficient Radio Access Network Sharing In Advanced Communication Networks is provided. A method includes facilitating, by a system comprising at least one processor, network energy savings in a communications network that is deployed in a shared radio access network architecture. The facilitating includes implementing, at a network operator level of the communications network according to a defined energy efficiency criterion, energy efficient scheduling of user equipment within the communications network. The facilitating also includes implementing, at an infrastructure provider level of the communications network, network energy savings actions based on respective measured quality of service levels of the user equipment being retained at or above a defined quality of service level. In an example, network equipment comprised by the communications network is configured to operate according to at least a fifth generation radio network communication protocol.

Abstract: Use of a data-driven approach that assimilates historical signal to interference-plus noise ratio (SINR) and channel estimation data along with location-map of the cell in which base station equipment is situated to better define the relationship between SINR and the user-channel environmental map and spatio-temporal changes to it to achieve more granular, cell site-specific modeling is disclosed herein. This data-driven approach estimates SINR using variational autoencoders. Variational encoders typically consist of two sections, an encoder section and decoder section. The encoder section learns the distribution on the low-dimensional latent space over the input data samples. The decoder section is a generative model that learns the joint distribution of the latent variables and input data.

Abstract: Management of RF channel configuration for a base station with regard to communication sessions associated with devices to achieve desirable communication performance and network energy savings (NES) can be enhanced. Configuration manager component (CMC) can employ traffic prediction to predict data traffic to be communicated between base station and device during a time period. CMC can determine, from a group of RF channel configuration modes, a subgroup of candidate modes that can satisfy defined performance criteria. CMC, employing a spatial power consumption model, can determine respective power consumption of respective candidate modes with regard to the data traffic predicted to be communicated during the time period, and can determine which candidate mode provides highest NES and/or rank the respective candidate modes based on respective NES. CMC can determine and select the candidate mode to use at base station based on candidate mode that provides the highest NES.

Abstract: Facilitating hierarchical network control for load-balanced network energy savings in advanced communication networks is discussed. A method includes based on a federated learning process, determining, by a system comprising at least one processor, a graphical representation of a communication network. The graphical representation identifies respective communications between a group of radio units and a radio access network intelligent controller. The method also includes, based on the graphical representation, facilitating, by the system, user equipment association that defines an action for a network traffic load balancing process according to a network energy savings criterion. The network traffic load balancing process transfers network traffic of a specified user equipment from a source cell of a group of cells of the communication network to a target cell of the group of cells.

Abstract: Facilitating admission control in advanced communication networks is provided. A method includes, based on receipt of a request from a user equipment for admission into a communication network, analyzing, by a system comprising at least one processor, information indicative of stochastic measurements of the communication network, resulting in an adaptive threshold. The method also includes, based on the adaptive threshold being determined to satisfy a defined admission control criterion, facilitating, by the system, admission of the user equipment into the communication network. Further, the method includes, based on the adaptive threshold being determined to fail to satisfy the defined admission control criterion, denying, by the system, the admission of the user equipment into the communication network.

Abstract: The technology described herein is directed towards accurate low-complexity decision tree-based traffic predictor models, such as decision tree regressor models for use by base stations. Each model is rapidly retrained per base station using data relevant to the base station. To improve accuracy, statistically significant feature data is selected by performing hypothesis testing on candidate features to filter out features that cannot satisfy a statistical significance threshold (e.g., p-value). The decision tree regressor model is recursively grown based on the selected features' feature values and their traffic level labels. Predicted traffic level data is determined by traversing the trained decision tree to reach a leaf node associated with the prediction data. Resource allocation can be based on the prediction. In addition to time-series training data, spatial training data can be used. Real time traffic monitoring by a radio unit for operating in an autonomous management mode is also facilitated.

Abstract: Energy efficient link adaptation (e.g., using a computerized tool), is enabled. For example, a system can comprise at least one processor and at least one memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise, for a communication link between network equipment and a user equipment, modeling network equipment power consumption applicable to the network equipment and modeling user equipment power consumption applicable to the user equipment, based on at least one model resulting from the modeling of the network equipment power consumption and the modeling of the user equipment power consumption, determining a link energy efficiency applicable to the communication link, and based on the link energy efficiency, performing link adaptation comprising modifying a set of link transmission parameters applicable to the communication link, wherein the modifying has been determined to increase the link energy efficiency.

Abstract: Facilitating generalized cell reselection for network energy savings using graph-based abstractions in advanced communication networks is provided. A method includes determining, by a system comprising at least one processor, respective results of application of an objective formulation to respective combinations of a specified user equipment of a source cell and respective target cells of a group of target cells. A communication network comprises the source cell and the group of target cells. The method also includes, based on the respective results of the application of the objective formulation, facilitating, by the system, user equipment association that defines an action for a network traffic load balancing process that transfers network traffic of the specified user equipment from the source cell to a target cell of the group of target cells.

Abstract: A system for enhancing an energy efficiency of a mobile network includes a digital twin connected to the mobile network. The digital twin is programmed to: receive, from the mobile network, real-time network traffic data and base station data of base stations operating in the mobile network; generate a first prediction of network traffic at a first time-scale based on the real-time network traffic data; generate a second prediction of network traffic at a second time-scale based on the real-time network traffic data and the base station data; and generate a first power consumption prediction for the mobile network based on the first prediction, the second prediction, and a topology of the mobile network, and the digital twin emulates at least a portion of the mobile network.

Abstract: Facilitating dynamic network traffic steering using artificial intelligence in advanced communication networks is provided. A method includes determining respective results of application of a utility function to respective combinations of potential handovers of a specified user equipment from a source cell to respective target cells of a group of target cells. The method also includes, based on the respective results of the application of the utility function, determining that a first combination of the respective combinations increases a value of the utility function as compared to other combinations of the respective combinations, other than the first combination. Further, the method includes, during a defined interval associated with a traffic steering process, facilitating the handover of the specified user equipment from the source cell to the first target cell. Other user equipment other than the specified user equipment within the communication network are not handed over during the defined interval.

Abstract: Facilitating dynamic network traffic steering using graph neural networks in advanced communication networks is provided. A method includes determining respective results of application of a utility function to all combinations of potential handovers of the specified user equipment from a source cell to respective target cells of a group of target cells. A communication network can include the source cell and the group of target cells. The operations can also include, based on the respective results of the application of the utility function, implementing a network traffic steering process that moves connectivity of the user equipment from the source cell to a single target cell of the group of target cells.

Abstract: Facilitating artificial intelligence enabled dynamic threshold-based cell and/or carrier switching for energy efficiency in advanced communication networks is provided. A method includes determining traffic load switching thresholds for respective cells of a group of cells of a communications network. The method also includes determining respective results of application of a utility function to the respective cells. Based on the traffic load switching thresholds and the respective results of the utility function, the method includes determining that a selected switching policy for a single cell of the group of cells satisfies a parameter of the utility function. In addition, the method includes facilitating implementing the selected switching policy for the single cell. Respective switching policies of other cells of the group of cells, other than the single cell, are not implemented during the implementing of the selected switching policy for the single cell.

Abstract: Facilitating concurrent energy aware admission control, dynamic load balancing, and traffic steering in advanced communication networks is provided. A method includes facilitating energy efficiency aware load balancing of already served user equipment to distribute the already served user equipment among a group of cells of a communication network. The method also includes facilitating controlling of admission of incoming user equipment to the communication network and facilitating traffic steering of the incoming user equipment among the group of cells. Further, the method includes facilitating conflict mitigation among respective policies associated with facilitating the energy efficiency aware load balancing, facilitating of the controlling of the admission, and facilitating of the traffic steering.

Abstract: Facilitating energy aware multi-cell admission control with dynamic load balancing in advanced communication networks is provided herein. A method includes facilitating, by a system comprising a processor, energy efficiency aware load balancing of already served user equipment. The method also includes facilitating, by the system, controlling of admissions of other user equipment to the communication network. The communication network can be deployed as a disaggregated architecture that comprises central units, distributed units, and a near-real-time-radio access network intelligent controller. The group of cells can be configured to operate according to a new radio network communication protocol.