Abstract: A method (200) is disclosed for orchestrating management of a plurality of operational parameters in an environment of a communication network. Each of the operational parameters is managed by a respective Agent, and at least one performance parameter of the communication network is operable to be impacted by each of the operational parameters. The method comprises obtaining a representation of a state of the environment (210), and generating a prediction, using an ML process and the obtained state representation, of which of the Agents, if allowed to execute within the environment an action selected by the Agent for management of its operational parameter, will result in the greatest increase of a performance measure for the communication network (220). The method further comprises selecting an Agent on the basis of the prediction (230) and initiating execution by the selected Agent of its selected action (240).

Abstract: A method of estimating, for an antenna of a base station, variations in received signal strength at User Equipments, UEs, the method comprising: transmitting, to a plurality of UEs, a reference signal; receiving, as input data: signal strength measurements indicating received reference signal strength of the reference signal from the UEs, and positional information from the UEs; generating model coefficients by processing the input data in a training model; and estimating variations in received signal strength of the received reference signal received at the UEs, the estimated variations corresponding to an electrical tilt change of the antenna, wherein the variations in received signal strength are estimated by: processing, in a prediction model, the generated model coefficients, a Remote Electrical Tilt, RET, increment which defines the electrical tilt change, and the positional information received from the UEs.

Abstract: A computer-implemented method for optimization of downlink (DL) transmit powers in a wireless communication network includes acquiring deployment data describing a deployment of a cluster of cells of the wireless communication network. Further, the method includes acquiring measurement data representing measurements performed with respect to a plurality of connections established between wireless devices and the wireless communication network. Further, the method includes, based on the deployment data and the measurement data, emulating effects of applying different DL transmit powers in at least some cells on the plurality of connections. Further, the method includes estimating state information for each of the cells based on the emulated effects. Further, the method includes, based on the estimated state information, training a machine learning (ML) model for optimization of the DL transmit powers of the cells.

Abstract: A method (50) and apparatus (60) for quantifying similarities between first and second cells (12a, 12b) in a mobile communications network (10) is provided. In particular, for each of the first cell and the second cell, a network node (60) receives input parameters, and based on those parameters, determines (54) respective sets of features for the cell. Each respective set of features defines a cell configuration and a coverage area morphology for the cell. So determined, a level of similarity between the first and second cells can be determined (56) based on their respective sets of features.

Abstract: A method and apparatus extend existing cell parameter optimization techniques so that they can be used in cases where different cells are controlled by the same control element. Particularly, a coordination agent (46) receives an intermediate control decision for controlling a control element (48) shared by the plurality of cells (14). The coordination agent consolidates each of the received intermediate decisions into a single consolidated control decision, and sends the consolidated control decision to the shared control element. The control element then controls a network element (26) in each of the cells using the same consolidated control decision.

Abstract: Methods and devices for signal strength prediction. In one aspect, a machine learning model is trained using physical cell information and geographic information to derive features corresponding to a region of a cell with a known signal strength value. The machine learning model can be used to predict signal strength values for other regions of the cell.

Abstract: Deviations of signal strengths in a first frequency band from signal strengths in at least one second frequency band are predicted based on a trained machine-learning model (350?). At least one source signal strength map is obtained. The at least one source signal strength map describes signal strengths in the at least one second frequency band for a coverage area of the wireless communication network. Based on the at least one source signal strength map and the predicted deviations of signal strengths, at least one target signal strength map describing signal propagation in the first frequency band for the coverage area is determined.

Abstract: According to an aspect, there is provided a computer-implemented method of training a policy for use by a reinforcement learning, RL, agent (406) in a communication network, wherein the RL agent (406) is for optimising one or more cell parameters in a respective cell (404) of the communication network according to the policy, the method comprising: (i) deploying (1001) a respective RL agent (408) for each of a plurality of cells (404) in the communication network, the plurality of cells (404) including cells that are neighbouring each other, each respective RL agent (408) having a first iteration of the policy; (ii) operating (1003) each deployed RL agent (408) according to the first iteration of the policy to adjust or maintain one or more cell parameters in the respective cell (404); (iii) receiving (1005) measurements relating to the operation of each of the plurality of cells (404); and (iv) determining (1007) a second iteration of the policy based on the received measurements relating to the operation of

Abstract: A method of monitoring a UMTS network is provided. The method comprises getting a recorded Radio Resource Control, RRC, message associated with a user and getting recorded data indicative of cell radio resource usage in the UMTS network. Further, the method comprises correlating the RRC message with the data indicative of cell radio resource usage. Further, the method comprises determining a measurement quantity indicated in the RRC message and, based on the measurement quantity indicated in the RRC message and the correlated data indicative of cell radio resource usage, calculating quality of a HSDPA radio channel for the user.

Abstract: A node (100, 200) of a cellular network obtains reports of measurements for a given cell (20) of the cellular network. The measurements are performed by one or more communication devices (10) associated with a surrounding cell (21) of the given cell. Each measurement indicates a received signal strength of a signal (RS) from the given cell (20). On the basis of the received signal strengths, the node (100) determines a level of interference generated by the given cell (20) with respect to the surrounding cell (21).

Abstract: Known HO performance indicators comprise Retainability Rate and HO Successful Rate. The Retainability Rate is defined as the ratio between abnormal connection releases and the total number of releases, and HO Successful Rate is defined as the ratio of successful HOs and the total number of HO attempts. The application proposes two new HO performance indicators LowCqiRateWhenHo (522) and LowUISinrRateWhenHo (524). These two HO performance indicators are based on radio link conditions before a HO for the DL and UL channels respectively. The indicator LowCqiRateWhenHo is defined as the ratio of low, i.e. below a threshold (514). CQI measurement samples of users (502) just before a HO and is as a measure of DL connection quality during HO. The indicator LowUISinrRateWhen Ho is defined as the ratio of low, i.e. below a threshold (516). SINR measurement samples (506) in UL just before a HO and is as a measure of UL connection quality during HO.

Abstract: Known HO performance indicators comprise Retainability Rate and HO Successful Rate. The Retainability Rate is defined as the ratio between abnormal connection releases and the total number of releases, and HO Successful Rate is defined as the ratio of successful HOs and the total number of HO attempts. The application proposes two new HO performance indicators LowCqiRateWhenHo (522) and LowUISinrRateWhenHo (524). These two HO performance indicators are based on radio link conditions before a HO for the DL and UL channels respectively. The indicator LowCqiRateWhenHo is defined as the ratio of low, i.e. below a threshold (514). CQI measurement samples of users (502) just before a HO and is as a measure of DL connection quality during HO. The indicator LowUISinrRateWhen Ho is defined as the ratio of low, i.e. below a threshold (516). SINR measurement samples (506) in UL just before a HO and is as a measure of UL connection quality during HO.

Abstract: A node (100, 200) of a cellular network obtains reports of measurements for a given cell (20) of the cellular network. The measurements are performed by one or more communication devices (10) associated with a surrounding cell (21) of the given cell. Each measurement indicates a received signal strength of a signal (RS) from the given cell (20). On the basis of the received signal strengths, the node (100) determines a level of interference generated by the given cell (20) with respect to the surrounding cell (21).