A method, a device, and a non-transitory storage medium are described in which a polymorphic algorithm service is provided. The service may coordinate and manage the execution of polymorphic algorithms of various optimization types in a multi-tier network, such as a radio access network or a self-organizing network. The service may coordinate the transition of execution of the polymorphic algorithms among the tiers of the multi-tier network, such that the polymorphic algorithm of a type may be active, for any given node, in a single tier of the multi-tier network. The service may monitor the yield of the optimizations based on various machine learning technologies, polices, and optimization targets. The polymorphic algorithms may operate in different time granularities in correspondence to the tiers of the multi-tier network.

A method, a device, and a non-transitory storage medium are described in which a polymorphic algorithm service is provided. The service may coordinate and manage the execution of polymorphic algorithms of various optimization types in a multi-tier network, such as a radio access network or a self-organizing network. The service may coordinate the transition of execution of the polymorphic algorithms among the tiers of the multi-tier network, such that the polymorphic algorithm of a type may be active, for any given node, in a single tier of the multi-tier network. The service may monitor the yield of the optimizations based on various machine learning technologies, polices, and optimization targets. The polymorphic algorithms may operate in different time granularities in correspondence to the tiers of the multi-tier network.

Disclosed here is a system and method to obtain a layer selection message from a wireless telecommunication network providing a wireless connection to a UE. The layer selection message includes a contour including an indication of a region served by a cell, an indication of a generation of wireless technology provided by the cell, a radio frequency band provided by the cell, and an indication of how to select the cell. The system can obtain a geographical location of the UE and a wireless capability of the UE. Based on the geographical location of the UE, the wireless capability of the UE, and the layer selection message, the system can determine a cell to which to connect the UE.

An access point that determines communication boundaries is described. During operation, the access point may receive one or more probe requests associated with the one or more electronic devices, where a given probe request may include an identifier of a given electronic device. Moreover, when the access point receives the given probe request, the access point may determine a signal strength associated with the given probe request and timestamps when the given probe request was received. Then, the access point may exchange probe-request information with one or more additional access points, where the probe-request information corresponds to received probe requests. Next, the access point may compare the probe-request information to determine the communication boundaries. For example, the communication boundaries may be determined by comparing the probe-request information for pairs of access points. Furthermore, the access point may identify neighboring access points based at least in part on the probe-request information.

Technologies are generally described for automatic adjustment of position and/or location of a customer premises equipment (CPE) to enhance signal quality under varying conditions. Following an initial setup of the CPE, conditions that may degrade signal quality may be monitored and correlated to degradation of the signal quality. Machine learning may be employed to determine new position(s)/location(s) for the CPE and its antenna to improve the signal quality between the CPE and its cellular base station.

Technologies are generally described for automatic adjustment of position and/or location of a customer premises equipment (CPE) to enhance signal quality under varying conditions. Following an initial setup of the CPE, conditions that may degrade signal quality may be monitored and correlated to degradation of the signal quality. Machine learning may be employed to determine new position(s)/location(s) for the CPE and its antenna to improve the signal quality between the CPE and its cellular base station.

Systems and methods are provided for enhancing cellular network coverage. In order to enhance network coverage, churn data is correlated with the measured coverage data for the multiple locations in the cellular network. Based on the correlation, a network readiness status in the multiple locations is determined. Network resources are then deployed to any of the multiple locations determined to have a network readiness status of not ready.

Systems and methods are provided for enhancing cellular network coverage. In order to enhance network coverage, churn data is correlated with the measured coverage data for the multiple locations in the cellular network. Based on the correlation, a network readiness status in the multiple locations is determined. Network resources are then deployed to any of the multiple locations determined to have a network readiness status of not ready.

Provided are: a first wireless station, a second wireless station that can move to an arbitrary position in a three-dimensional space and perform wireless communication with the first wireless station, and a measuring device that measures the position of the second wireless station in the three-dimensional space to obtain measurement information. At least one out of the first wireless station, the second wireless station, and the measuring device includes: an acquisition unit that acquires reception signal information that was received by the first wireless station or the second wireless station, and a calculation unit that associates the measurement information and the reception signal information with each other and calculates the propagation characteristics between the first wireless station and the second wireless station.

Provided are: an extraction unit that, based on input information from an external source, extracts position information regarding a transmitting station and a receiving station and the frequency of radio waves transmitted by the transmitting station; a calculation unit that calculates the three-dimensional distance between the transmitting station and the receiving station and the free space loss of radio waves based on the position information and the frequency; a processing unit that performs processing for, with use of a two-dimensional image of a region that includes the transmitting station and the receiving station, extracting the edges of structures that reflect radio waves in the two-dimensional image; a generation unit that generates input data for use in machine learning, based on the position information, the three-dimensional distance, the free space loss, and the edge; and a learning unit that performs machine learning to estimate the propagation characteristics of radio waves between the transmitting station and the receiving station with use of the input data generated by the generation unit.