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EP-4736527-A1 - METHODS AND DEVICES FOR MULTI-CELL RADIO RESOURCE MANAGEMENT ALGORITHMS

EP4736527A1EP 4736527 A1EP4736527 A1EP 4736527A1EP-4736527-A1

Abstract

A device may include a memory configured to store an artificial intelligence or machine learning model (AI/ML) configured to provide an output used in radio resource management of a plurality of cells; and a processor configured to: obtain cell-specific parameters of the plurality of cells of a mobile communication network; select a subset of the plurality of cells based on obtained cell-specific parameters; and cause the AI/ML to be trained with radio access network (RAN)-related data of the subset of the plurality of cells.

Inventors

  • SINGH, VAIBHAV
  • MACIOCCO, CHRISTIAN

Assignees

  • INTEL Corporation

Dates

Publication Date
20260506
Application Date
20231228

Claims (20)

  1. 1. A device comprising: a memory configured to store an artificial intelligence or machine learning model (AI/ML) configured to provide an output used in radio resource management of a plurality of cells; a processor configured to: obtain cell-specific parameters of the plurality of cells of a mobile communication network; select a subset of the plurality of cells based on obtained cell-specific parameters; and cause the AI/ML to be trained with radio access network (RAN)-related data of the subset of the plurality of cells.
  2. 2. The device of claim 1, wherein the processor is further configured to selectively cause the AI/ML to be trained with first data including the RAN-related data of the subset of the plurality of cells or cause the AI/ML to be trained with second data including RAN-related data of at least one or more cells that are not within the subset of the plurality of cells.
  3. 3. The device of claim 2, wherein the processor is further configured to cause the AI/ML to be trained with the first data for a first period of time and cause the AI/ML to be trained with data including the second data for a second period of time.
  4. 4. The device of claim 2, wherein the processor is further configured to cause the AI/ML to be trained with the first data more frequently than to cause the AI/ML to be trained with the second data.
  5. 5. The device of claim 2, wherein the processor is further configured to cause the first data to be sampled continuously from first network access nodes of the subset of the plurality of cells and to cause the RAN-related data of the at least one or more cells that are not within the subset of the plurality of cells to be sampled intermittently from second network access nodes.
  6. 6. The device of any one of claims 1 to 5, wherein the processor is further configured to aggregate the RAN-related data of the subset of the plurality of cells to obtain training data used to train the AI/ML.
  7. 7. The device of any one of claims 1 to 6, wherein the processor is further configured to select the subset based on operator information representative of a preference of a mobile network operator.
  8. 8. The device of claim 7, wherein the operator information comprises information representative of at least one of usable priority cells, one or more performance thresholds associated with one or more performance metrics, a number of cells in the subset, one or more cost metrics, or a preference for optimization.
  9. 9. The device of any one of claims 1 to 8, wherein cell-specific parameters of each cell comprises information representative of at least one of network traffic, downlink traffic, uplink traffic, physical resource block (PRB) usage, reference signal strength indicator (RS SI), reference signal receive power (RSRP), data throughput, mobility, user density, geolocation, topography, traffic patterns, user equipment (UE) distribution, a number of UEs in an RRC connected state, a number of active users, user channel quality summary, or UE density.
  10. 10. The device of any one of claims 1 to 9, wherein the processor is further configured to select the subset of the plurality of cells based on AI/ML information representative of features of the AI/ML.
  11. 11. The device of claim 10, wherein the AI/ML information comprises information representative of at least one of exemplary cells of the plurality of cells, a performance requirement of the AI/ML model, a computation requirement of the AI/ML model, a data aggregation requirement to train the AI/ML model, a weighting parameter associated with performance and cost of operation, a mapping associated with the performance of the AI/ML and the cost of operation of the AI/ML, one or more requirements associated with input data of the AI/ML.
  12. 12. The device of any one of claims 1 to 11, wherein the processor is further configured to determine exemplary cells of the plurality of cells based on a cell selection criterion.
  13. 13. The device of any one of claims 10 to 12, wherein the processor is further configured to calculate similarity scores for multiple subsets of the cells, each calculated similarity score is representative of a similarity between one or more cell-specific parameters of cells of a respective subset and one or more cell-specific parameters of the exemplary cells.
  14. 14. The device of any one of claims 10 to 12, wherein the processor is further configured to determine exemplary cells of the plurality of cells iteratively by adding a cell of the plurality of cells into the exemplary cells of the plurality of cells and performance of the AI/ML output of the iterations; wherein the processor is further configured to calculate similarity scores for multiple subsets of the cells, each calculated similarity score is representative of a similarity between one or more cell-specific parameters of cells of a respective subset and one or more cell-specific parameters of the exemplary cells.
  15. 15. The device of claim 13 or claim 14, wherein the subset of the plurality of cells is selected from the plurality of subsets of the cells using a greedy approach that maximizes a measure with a cardinality constraint for number of cells within each subset of the multiple subsets of the cells; wherein the measure being I, the processor is configured to select A being the subset of the plurality of cells according to Q being the exemplary cells with a cardinality constraint | A|<b based on the similarity mapping operation Si,j denoting the mapping between i-th cellspecific parameters of the A and j -th cell-specific parameters of the Q; and wherein the greedy approach is used to identify the A that maximizes the I.
  16. 16. The device of any one of claims 10 to 12, wherein the processor is further configured to select the subset of the plurality of cells based on exemplary cells using a reinforcement learning model; wherein a reward of the reinforcement learning (RL) model is based on a performance metric of the AI/ML and a cost metric of the AI/ML.
  17. 17. The device of claim 16, wherein the processor is further configured to determine a state based on the cell-specific parameters of at least the exemplary cells; wherein the processor is further configured to determine an action by adding one or more further cells from the plurality of cells to a set comprising the exemplary cells.
  18. 18. The device of any one of claims 1 to 17, wherein the processor is further configured to implement the AI/ML.
  19. 19. The device of any one of claims 1 to 17, wherein the AI/ML is implemented by a controller that is external to the processor; wherein the processor is further configured to provide, to the controller, information representative of the subset of the plurality of cells; wherein the controller is configured to train the AI/ML using the RAN-related data of the subset of the plurality of cells.
  20. 20. The device of any one of claims 1 to 19, wherein the mobile communication network comprises an open radio access network (0-RAN); wherein the device is configured to implement a radio access network intelligent controller (RIC); wherein the RIC comprises a near real-time RIC or a non-real time RIC.

Description

METHODS AND DEVICES FOR MULTI-CELL RADIO RESOURCE MANAGEMENT ALGORITHMS Technical Field [0001] This disclosure generally relates to methods and devices for training artificial intelligence or machine learning models (AI/ML) for radio resource management and using such an AI/ML. Background [0002] In mobile radio communication networks, in accordance with many mobile radio communication technologies, such as Fourth Generation (LTE) and Fifth Generation (5G) New Radio (NR) and upcoming next generation radios, there are various techniques that are applied to manage radio resources. Such techniques may include controlling parameters associated with scheduling transmission of radio communication signals, transmit power, allocation of mobile communication devices within radio resources, beamforming, data rates for communications, handover functions, modulation and coding schemes, etc. [0003] Radio resource managing entities of a mobile communication network may manage radio resources within the mobile communication network using radio resource management models employing various algorithms, such as artificial intelligence or machine learning models, to obtain and select parameters associated with the management of the radio resources. Due to varying conditions within the mobile communication network, a radio resource management model may be updated from time to time in order to fit the radio resource management model to the conditions of the mobile communication network. Brief Description of the Drawings [0004] In the drawings, like reference characters generally refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the disclosure. In the following description, various aspects of the disclosure are described with reference to the following drawings, in which: FIG. 1 shows an exemplary radio communication network; FIG. 2 shows an exemplary internal configuration of a communication device; FIG. 3 shows an exemplary illustration of cells of a mobile communication network; FIG. 4 shows an example of a device according to various examples in this disclosure; FIG. 5 shows an exemplary illustration of cell data; FIG. 6 shows an exemplary illustration of RAN data; FIG. 7 shows an example of a processor and a memory of a device according to various aspects provided in this disclosure; FIG. 8 shows an exemplary illustration of training an AI/ML in accordance with various aspects provided herein; FIG. 9 shows an exemplary illustration of communication paths between network access nodes and a device in accordance with various aspects provided herein; FIG. 10 shows an exemplary illustration of selecting exemplary cells from a plurality of cells; FIG. 11 shows an exemplary procedure including selecting cells from a plurality of cells; FIG. 12 shows an exemplary representation of a reinforcement learning (RL) model based AI/ML; FIG. 13 shows an exemplary illustration of various entities of a mobile communication network; FIG. 14 shows an exemplary radio access network architecture in which the radio access network is disaggregated into multiple units; FIG. 15 shows an example of an AI/ML; FIG. 16 shows an example of a method; and FIG. 17 shows an example of a method. Description [0005] The following detailed description refers to the accompanying drawings that show, by way of illustration, exemplary details, and aspects in which aspects of the present disclosure may be practiced. [0006] With exposure to terabytes (TBs) of data from the multiple radio access network (RAN) cells, different AI/ML-based Radio Resource Management (RRM) algorithms may be designed to learn from the network (e.g. RAN) load patterns, user patterns, network data traffic patterns, wireless environment patterns, and/or device mobility patterns to optimize the operation of one or multiple RANs. For example, RRM models associated with load balancing, CQI (Channel Quality Indicator) period optimization, connectivity optimization, and optimization of RAN resources such as MIMO usage, sub-band (frequency) usage, energy saving, etc. can be further optimized to support the workloads after learning past behaviors supported by the RAN and estimations/predictions/classifications, etc. accordingly with a level of uncertainty, sometimes in a near future. Such learning and predictions may be employed by training AI/MLs. [0007] In some aspects, an AI/ML based (or AI/ML assisted) RRM algorithm may be used for managing operations of multiple networks and respective access nodes associated with (e.g. serving to) multiple cells of a cellular network. For this purpose, an AI/ML may be deployed in a network architecture of multiple cells at an entity that may communicate with multiple network access nodes in order to exchange information, such as to receive data to be used as input to the AI/ML and to provide information for the management of radio reso