Systems and methods that use historical data comprising capacity gain solutions and their associated gains at various locations to train a machine learning model. The trained machine learning model, upon receiving a new location (e.g., latitude and longitude coordinates), recommends the top n (e.g., the top 3) solutions that should be deployed at the new location to improve telecommunications network performance. The machine learning model uses clustering techniques to perform the recommendations.

Systems and methods that use historical data comprising capacity gain solutions and their associated gains at various locations to train a machine learning model. The trained machine learning model, upon receiving a new location (e.g., latitude and longitude coordinates), recommends the top n (e.g., the top 3) solutions that should be deployed at the new location to improve telecommunications network performance. The machine learning model uses clustering techniques to perform the recommendations.

This disclosure provides systems, methods and apparatuses for measurement of cells and determination of a non-standalone (NSA) coverage status using an NSA coverage database. The NSA coverage database may indicate candidate frequencies of New Radio (NR) cells that provide NSA coverage corresponding to a given Long Term Evolution (LTE) serving cell. The NSA coverage database may also indicate whether a system information block 1 (SIB1) has been previously received on a candidate frequency. The user equipment (UE) may perform measurement of candidate frequencies using the NSA coverage database, and may identify cases where a cell not providing NSA coverage has a same candidate frequency as a cell providing NSA coverage using the indication of whether SIB1 has been previously received on a candidate frequency.

This disclosure provides systems, methods and apparatuses for measurement of cells and determination of a non-standalone (NSA) coverage status using an NSA coverage database. The NSA coverage database may indicate candidate frequencies of New Radio (NR) cells that provide NSA coverage corresponding to a given Long Term Evolution (LTE) serving cell. The NSA coverage database may also indicate whether a system information block 1 (SIB1) has been previously received on a candidate frequency. The user equipment (UE) may perform measurement of candidate frequencies using the NSA coverage database, and may identify cases where a cell not providing NSA coverage has a same candidate frequency as a cell providing NSA coverage using the indication of whether SIB1 has been previously received on a candidate frequency.

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.

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.

Example embodiments are directed towards data delivery automation of a cloud-managed wireless telecommunication network. A disaggregated data construct is provided in a cloud-native, Open Radio Access Network (O-RAN), fifth-generation New Radio (5G NR) cellular telecommunication network. MNO cloud-native, O-RAN, 5G cellular telecommunication network engines are electronically mapped to components or services of a disaggregated network orchestrator. In an example embodiment, providing the disaggregated data construct may include electronically generating a mapping, via an open application programming interface (API), between a mobile network operator (MNO) cloud-native, O-RAN, 5G NR cellular telecommunication network service disaggregated slice design engine and a disaggregated intent engine of a disaggregated cellular telecommunication network orchestrator. The system operates the cloud-native, O-RAN, 5G NR cellular telecommunication network using the disaggregated data construct.

Example embodiments are directed towards data delivery automation of a cloud-managed wireless telecommunication network. A disaggregated data construct is provided in a cloud-native, Open Radio Access Network (O-RAN), fifth-generation New Radio (5G NR) cellular telecommunication network. MNO cloud-native, O-RAN, 5G cellular telecommunication network engines are electronically mapped to components or services of a disaggregated network orchestrator. In an example embodiment, providing the disaggregated data construct may include electronically generating a mapping, via an open application programming interface (API), between a mobile network operator (MNO) cloud-native, O-RAN, 5G NR cellular telecommunication network service disaggregated slice design engine and a disaggregated intent engine of a disaggregated cellular telecommunication network orchestrator. The system operates the cloud-native, O-RAN, 5G NR cellular telecommunication network using the disaggregated data construct.

A wireless communication network comprises access points distributed across a geographical area and configured to wirelessly communicate with distributed user devices in the area, and a central server communicatively connected to the access points and configured to control the network. The access points are grouped, based on channel state information, to form a plurality of communication clusters each in wireless communication with a subset of the user devices in geographically proximal location thereto, and each communication cluster and its subset of user devices forms a subnetwork. The subnetworks are arranged for wireless communication in nonoverlapping portions of the geographical area. The access points of a common subnetwork are configured to wirelessly exchange data with the subnetwork’s user devices using a common frequency range. Each communication cluster comprises an edge computing device formed by one or more of the access points belonging thereto and configured to exchange data with the server.

A wireless communication network comprises access points distributed across a geographical area and configured to wirelessly communicate with distributed user devices in the area, and a central server communicatively connected to the access points and configured to control the network. The access points are grouped, based on channel state information, to form a plurality of communication clusters each in wireless communication with a subset of the user devices in geographically proximal location thereto, and each communication cluster and its subset of user devices forms a subnetwork. The subnetworks are arranged for wireless communication in nonoverlapping portions of the geographical area. The access points of a common subnetwork are configured to wirelessly exchange data with the subnetwork’s user devices using a common frequency range. Each communication cluster comprises an edge computing device formed by one or more of the access points belonging thereto and configured to exchange data with the server.