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: There is provided a method for determining a suitability of a cell of a network for deployment as a Multiple-Input Multiple-Output (MIMO) cell. The method includes acquiring data associated with a cell of a network and processing the acquired data associated with the cell of the network using a first machine learnt model to obtain one or more metrics indicative of the suitability of the cell for deployment as a MIMO cell. The method also includes generating an indication of whether the cell is suitable for deployment as a MIMO cell based on the one or more obtained metrics.

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 for detecting swapped antenna sectors in a cellular communications network. For each of one or more cells in the cellular communications network, an azimuth is estimated for each of two or more antenna sectors in the cell using a plurality of geo-located signal measurements for each antenna sector and a machine-learning algorithm. The estimated azimuths are compared to azimuths associated with the corresponding antenna sectors in a stored representation of the cellular communications network, to detect swapped antenna sectors in the cell.

Abstract: There is provided a method for determining a suitability of a cell of a network for deployment as a Multiple-Input Multiple-Output (MIMO) cell. The method includes acquiring data associated with a cell of a network and processing the acquired data associated with the cell of the network using a first machine learnt model to obtain one or more metrics indicative of the suitability of the cell for deployment as a MIMO cell. The method also includes generating an indication of whether the cell is suitable for deployment as a MIMO cell based on the one or more obtained metrics.

Abstract: Concepts and technologies are disclosed herein for identifying backup coverage in wireless networks. An active network resource to be analyzed is identified, and a at least one potential backup network resource is identified. The at least one potential backup network resource is evaluated to determine if the at least one potential backup network resource is available to function as the backup coverage for the active network resource. If the at least one potential backup resource is available to function as the backup coverage for the active network resource, a power management parameter associated with the wireless networking environment can be updated.

Abstract: Technologies are described herein for parameter optimization of at least one interlayer handover in a multilayer wireless cellular communication network. Performance information for the communication network is retrieved. The retrieved performance information for the communication network is then averaged over a predetermined period of time. A determination is made based on the performance information as to whether optimization of the communication network is required. If so, the interlayer handover is optimized by capturing a current set of configuration parameters for the interlayer handover, generating a new set of configuration parameters for the interlayer handover based on the retrieved performance information and the current set of configuration parameters, and applying the new set of configuration parameters to the communication network.

Abstract: Technologies are described herein for real time event-driven automation of energy management features within a wireless communications network. Resources within a wireless communications network, such as entire base stations or equipment within a base station supporting specific cells, sectors, frequency bands, or services, may be switched on or off in response to events occurring within the communications network. A central controller receives a network interface message corresponding to an event occurring within the communications network. The central controller analyzes the network interface message and, based upon the analysis of the network interface message, generates an action message that includes instructions to take an action on an affected network resource within the communications network. The action message is then transmitted to the appropriate network resource.

Abstract: Technologies are described herein for parameter optimization of at least one interlayer handover in a multilayer wireless cellular communication network. Performance information for the communication network is retrieved. The retrieved performance information for the communication network is then averaged over a predetermined period of time. A determination is made based on the performance information as to whether optimization of the communication network is required. If so, the interlayer handover is optimized by capturing a current set of configuration parameters for the interlayer handover, generating a new set of configuration parameters for the interlayer handover based on the retrieved performance information and the current set of configuration parameters, and applying the new set of configuration parameters to the communication network.

Abstract: Concepts and technologies are disclosed herein for identifying a network resource in a wireless networking environment as essential. A network resource to be tested is identified. The network resource is transitioned to a softbar operation state. In the softbar operation state, use of the network resource is avoided. If access data indicates substantial use of the network resource, the network resource can be identified as essential.

Abstract: Concepts and technologies are disclosed herein for identifying backup coverage in wireless networks. An active network resource to be analyzed is identified, and a at least one potential backup network resource is identified. The at least one potential backup network resource is evaluated to determine if the at least one potential backup network resource is available to function as the backup coverage for the active network resource. If the at least one potential backup resource is available to funtion as the backup coverage for the active network resource, a power management parameter associated with the wireless networking environment can be updated.

Abstract: Technologies are described herein for improving call quality and coverage of cells within a mobile wireless communication network. Such improvements can be accomplished by adjusting various radio access network parameters. The adjustments may be made at the cell level or at the neighbor level. An iterative process can periodically collect key performance indicator (KPI) statistics from the mobile wireless network. System improvements may derive from one or more rules applied in parallel. Each rule can have a unique combination of minimum or maximum KPI thresholds. System issues may be identified when a cell correlates with one or more of the rules which may then suggest one or more parameter changes to reduce the identified system issue. System capacity policies may be provided as limits to the coverage and call quality triggers.

Abstract: Technologies for load balancing among neighboring cells of a mobile wireless communication network can reduce traffic congestion and improve network system capacity. Load balancing can be accomplished by adjusting various radio access network parameters. Such adjustments may be made at the cell level or at the neighboring cell level. The adjustments can be applied iteratively in response to various collected operational statistics. The adjustments can adapt cell size and cell shape as well as adapt handover to maximize system resource and hardware utilization. An iterative process of optimization can periodically collect performance statistics and network configuration from a mobile wireless network. The collected information can be periodically analyzed to determine parameter adjustments. Configuring additional capacity from the communication network can prevent or substantially delay the acquisition of additional hardware resources to mitigate system capacity issues.

Abstract: Technologies are described herein for real time event-driven automation of energy management features within a wireless communications network. Resources within a wireless communications network, such as entire base stations or equipment within a base station supporting specific cells, sectors, frequency bands, or services, may be switched on or off in response to events occurring within the communications network. A central controller receives a network interface message corresponding to an event occurring within the communications network. The central controller analyzes the network interface message and, based upon the analysis of the network interface message, generates an action message that includes instructions to take an action on an affected network resource within the communications network. The action message is then transmitted to the appropriate network resource.

Abstract: Technologies are described herein for improving call quality and coverage of cells within a mobile wireless communication network. Such improvements can be accomplished by adjusting various radio access network parameters. The adjustments may be made at the cell level or at the neighbor level. An iterative process can periodically collect key performance indicator (KPI) statistics from the mobile wireless network. System improvements may derive from one or more rules applied in parallel. Each rule can have a unique combination of minimum or maximum KPI thresholds. System issues may be identified when a cell correlates with one or more of the rules which may then suggest one or more parameter changes to reduce the identified system issue. System capacity policies may be provided as limits to the coverage and call quality triggers.