The present disclosure provides a big-data-mining-based wireless channel modeling method comprising: obtaining image information of a measurement environment and a channel impulse response data sample under a preset condition; obtaining at least one multipath wave and a channel parameter of each of the multipath wave according to the channel impulse response data sample using a channel parameter estimation algorithm; and clustering the at least one multipath wave according to the channel parameter of each of the multipath wave using a clustering algorithm to obtain at least one cluster; obtaining at least one scattering object in the measurement environment according to the image information of the measurement environment; matching each of the cluster with each of the scattering object to obtain a cluster kernel which is a cluster matching with the scattering object; establishing, a base wireless channel model under the preset condition according to all of the cluster kernel.

The technologies described herein are generally directed to facilitate allocating resources to zones for IOT equipment in a fifth generation (5G) network or other next generation networks. An example method discussed herein includes identifying, by carrier allocation equipment, carrier transmission information corresponding to transmission of a first carrier signal configured to support Internet of things equipment. The method can further comprise analyzing, by the carrier allocation equipment, the carrier transmission information to determine coverage information corresponding to a potential for coverage, by the first carrier signal, of an Internet of things equipment support zone corresponding to a geographic area. The method can further include, based on the coverage information, facilitating configuring transmission parameter information, representative of a transmission parameter applicable to the coverage of the Internet of things equipment support zone by the first carrier signal.

The technologies described herein are generally directed to facilitate allocating resources to zones for IOT equipment in a fifth generation (5G) network or other next generation networks. An example method discussed herein includes identifying, by carrier allocation equipment, carrier transmission information corresponding to transmission of a first carrier signal configured to support Internet of things equipment. The method can further comprise analyzing, by the carrier allocation equipment, the carrier transmission information to determine coverage information corresponding to a potential for coverage, by the first carrier signal, of an Internet of things equipment support zone corresponding to a geographic area. The method can further include, based on the coverage information, facilitating configuring transmission parameter information, representative of a transmission parameter applicable to the coverage of the Internet of things equipment support zone by the first carrier signal.

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.

Disclosed is a virtual wireless base station that can dynamically scale its capacity to meet changes in demand for connectivity. The virtual wireless base station includes a plurality of virtual baseband modules, a plurality of interface/router modules, an orchestrator module and a fabric mapper module. Each of the plurality of virtual baseband modules is coupled to the interface/router modules by a low latency switch fabric. The orchestrator determines current and near future demand for connectivity within the virtual wireless base station and either instantiates and connects new virtual baseband processors to meet a rise in demand, or shuts down underutilized virtual baseband processors in case of insufficient demand.

Disclosed is a virtual wireless base station that can dynamically scale its capacity to meet changes in demand for connectivity. The virtual wireless base station includes a plurality of virtual baseband modules, a plurality of interface/router modules, an orchestrator module and a fabric mapper module. Each of the plurality of virtual baseband modules is coupled to the interface/router modules by a low latency switch fabric. The orchestrator determines current and near future demand for connectivity within the virtual wireless base station and either instantiates and connects new virtual baseband processors to meet a rise in demand, or shuts down underutilized virtual baseband processors in case of insufficient demand.

A computing device is configured to: obtain information of tracking areas including a first and second tracking area, the first tracking area comprising first cells and the second tracking area comprising second cells; generate a user interface with a visualization of the tracking areas, the user interface comprising first cell user interface elements visually representing the first cells and second cell user interface elements visually representing the second cells; output the user interface for display at a display device; receive user input indicative of filtering criteria; generate a modified user interface by modifying at least one of the first cell user interface elements or the second user interface elements to visually indicate the first tracking area satisfies the filtering criteria and the second tracking area does not satisfy the filtering criteria; and output the modified user interface for display at the display device.

A computing device is configured to: obtain information of tracking areas including a first and second tracking area, the first tracking area comprising first cells and the second tracking area comprising second cells; generate a user interface with a visualization of the tracking areas, the user interface comprising first cell user interface elements visually representing the first cells and second cell user interface elements visually representing the second cells; output the user interface for display at a display device; receive user input indicative of filtering criteria; generate a modified user interface by modifying at least one of the first cell user interface elements or the second user interface elements to visually indicate the first tracking area satisfies the filtering criteria and the second tracking area does not satisfy the filtering criteria; and output the modified user interface for display at the display device.

Aspects of the subject disclosure may include, for example, determining a coherence block for each user equipment (UE) of a plurality of UEs being served by the first cell, resulting in a plurality of coherence blocks, responsive to the determining, identifying a smallest coherence block from the plurality of coherence blocks, identifying a pilot sequence length based on the smallest coherence block, determining a plurality of orthogonal pilot sequences based on the identifying the pilot sequence length, designating, from the plurality of orthogonal pilot sequences, a first group of orthogonal pilot sequences for use in the first cell, and distributing, to each neighboring cell of a plurality of neighboring cells adjacent to the first cell, a respective group of orthogonal pilot sequences from a remainder of the plurality of orthogonal pilot sequences, to prevent pilot contamination between the first cell and the plurality of neighboring cells. Other embodiments are disclosed.