The described technology is generally directed towards a cellular network area optimizer. The area optimizer observes cellular network conditions at multiple radio access network (RAN) nodes within a target area. Based on observed conditions, the area optimizer applies a set of parameter values at the multiple RAN nodes. The set of parameter values enhances the overall throughput, while maintaining or improving connection retainability and accessibility, of the multiple RAN nodes under the observed conditions. The area optimizer learns different sets of parameter values to apply in response to different observed conditions by making parameter value adjustments and observing the effect of the adjustments on overall throughput of the RAN nodes in the target area.

The described technology is generally directed towards a cellular network area optimizer. The area optimizer observes cellular network conditions at multiple radio access network (RAN) nodes within a target area. Based on observed conditions, the area optimizer applies a set of parameter values at the multiple RAN nodes. The set of parameter values enhances the overall throughput, while maintaining or improving connection retainability and accessibility, of the multiple RAN nodes under the observed conditions. The area optimizer learns different sets of parameter values to apply in response to different observed conditions by making parameter value adjustments and observing the effect of the adjustments on overall throughput of the RAN nodes in the target area.

Neighbor relation conflict prediction is performed by operations including receiving, from a serving MCG of a terminal, a measurement report of the terminal including a plurality of signal measurements associated with an SCG represented by a PCI and an ARFCN, identifying an unlisted SCG among the plurality of signal measurements, identifying one or more nearby MCG within a threshold distance of the serving MCG, counting a number of SCG in the NRT of each nearby MCG having the PCI and the ARFCN of the unlisted SCG, applying a classification model to the counted number of SCG and an MCG-PCI-ARFCN identifier representing the serving MCG, the PCI, and the ARFCN to obtain a binary value indicating whether provisioning the unlisted SCG with the serving MCG and the plurality of nearby MCG will result in PCI conflict.

Neighbor relation conflict prediction is performed by operations including receiving, from a serving MCG of a terminal, a measurement report of the terminal including a plurality of signal measurements associated with an SCG represented by a PCI and an ARFCN, identifying an unlisted SCG among the plurality of signal measurements, identifying one or more nearby MCG within a threshold distance of the serving MCG, counting a number of SCG in the NRT of each nearby MCG having the PCI and the ARFCN of the unlisted SCG, applying a classification model to the counted number of SCG and an MCG-PCI-ARFCN identifier representing the serving MCG, the PCI, and the ARFCN to obtain a binary value indicating whether provisioning the unlisted SCG with the serving MCG and the plurality of nearby MCG will result in PCI conflict.

Disclosed is a system for tracking and dynamically allocating wireless capacity within a wireless telecommunications network. The system has a plurality of processor levels: a layer of baseband-level capacity processors that are deployed within each baseband processor; a layer of client-level capacity processors that are deployed within each wireless base station; a layer of server-level capacity processors, each of which orchestrate allocation of wireless capacity over a unique domain of wireless base stations; and a master level capacity processor. Wireless capacity is allocated in terms of active connections to wireless devices, and the active connections are quantized and allocated as logical connections, or connection tokens. The system dynamically allocates wireless capacity so that resources are devoted to venues and environments where demand is greatest at any given time.

Disclosed is a system for tracking and dynamically allocating wireless capacity within a wireless telecommunications network. The system has a plurality of processor levels: a layer of baseband-level capacity processors that are deployed within each baseband processor; a layer of client-level capacity processors that are deployed within each wireless base station; a layer of server-level capacity processors, each of which orchestrate allocation of wireless capacity over a unique domain of wireless base stations; and a master level capacity processor. Wireless capacity is allocated in terms of active connections to wireless devices, and the active connections are quantized and allocated as logical connections, or connection tokens. The system dynamically allocates wireless capacity so that resources are devoted to venues and environments where demand is greatest at any given time.

A client device is configured to communicate with an access point over a wireless network, exchanging data with the access point over a selected communication channel. The client device stores an identifier of the selected communication channel. After the wireless connection to the access point has ended, the client device initiates a process to reconnect to the access point over the selected communication channel using the stored identifier.

Systems and methods are provided for leveraging uplink (U)L scheduling information obtained by way of Quality of Service (QoS) Null/QoS Data frames and Media Access Control (MAC) headers transmitted by any station (STA). This UL scheduling information can be read by any access point (AP) in an extended service set (ESS), and used to improve network performance. For example, an AP may use such UL scheduling information (from STAs associated to co-channel APs in the same ESS) to minimize the latency for UL latency-sensitive traffic in that ESS. Additionally, an AP may use such UL scheduling information to minimize contention amongst STAs connected to different APs on the same channel in the same ESS, thereby improving system capacity.

Systems and methods are provided for leveraging uplink (U)L scheduling information obtained by way of Quality of Service (QoS) Null/QoS Data frames and Media Access Control (MAC) headers transmitted by any station (STA). This UL scheduling information can be read by any access point (AP) in an extended service set (ESS), and used to improve network performance. For example, an AP may use such UL scheduling information (from STAs associated to co-channel APs in the same ESS) to minimize the latency for UL latency-sensitive traffic in that ESS. Additionally, an AP may use such UL scheduling information to minimize contention amongst STAs connected to different APs on the same channel in the same ESS, thereby improving system capacity.

Disclosed is a 5.sup.th generation (5G) or a pre-5G communication system provided to support a higher data transmission rate than that of post-4.sup.th generation (4G) communication systems, such as long term evolution (LTE). A method of operating a network node in a wireless communication system is provided. The method includes receiving, from a plurality of first network nodes, network data, generating first recommendation operation information for a second network node based on the network data, and transmitting, to the second network node, a first analysis result message including the first recommendation operation information.