The invention relates to method for configuring a first wireless network that includes at least one access point. In order to optimize the configuration of the first wireless network, the method comprises a step of configuring at least one parameter of the at least one access point of the first wireless network on the basis of at least a portion of the linking balance of a second wireless network including at least two nodes, the nodes being positioned in a predetermined physical space.

Determining a location of a user device comprises a wireless system supported by wireless access points receiving signals from a user device. The wireless system estimates a location of the user device based on a coarse calculation based on an angle of arrival of the received signal and determines if the user device is in an area under a access point. If so, the wireless system performs a fine location calculation. The wireless system identifies received signal strength indicator values for the user device and uses the values and the calculated location of the user device to improve calibration of a received signal strength location model. If the system determines that the user is not in the area under the access point, the wireless system combines the coarse calculation location and a received signal strength determined location and estimates a location based on the combined calculations.

A method for estimating communication quality of a wireless network applied to an estimation device having a storage device and a processor is provided. The method includes the steps of: providing coordinate information of a plurality of nodes and at least two kinds of map data; associating the coordinate information of the nodes with the map data to map the nodes to the corresponding positions of the maps; extracting spatial feature data between any two nodes from the associated map data, wherein the spatial feature data include spatial distribution data and spatial attribute data; selecting one of a plurality of path loss models according to the spatial feature data between the two nodes and estimating a pass loss using the selected path loss model, thereby estimating the communication quality between the two nodes.

Empirically modulated antenna systems and related methods are disclosed herein. An empirically modulated antenna system includes an antenna and a controller programmed to control the antenna. The antenna includes a plurality of discrete scattering elements arranged in a one- or two-dimensional arrangement. A method includes modulating operational states of at least a portion of a plurality of discrete scattering elements of the antenna in a plurality of different modulation patterns. The plurality of different modulation patterns includes different permutations of the discrete scattering elements operating in different operational states. The method also includes evaluating a performance parameter of the antenna responsive to the plurality of different empirical one- or two-dimensional modulation patterns. The method further includes operating the antenna in one of the plurality of different one- or two-dimensional empirical modulation patterns selected based, at least in part, on the performance parameter.

A method for assessing an impact of a design choice on a system level performance metric of a radio access network (RAN) deployed in an environment includes receiving messages from a plurality of UEs over time by a plurality of RNs in the RAN. A design choice is selected for a set of operating parameters of the RAN. One or more of measurement values in each of the received messages and the selected design choice are processed to compute a set of derivatives. A system level performance metric is determined as a function of the computed set of derivatives.

Systems and methods determine occupancy of a mobile device in a room based on one or more wireless signals measured by the mobile device. The mobile device receives and processes one or more wireless signals detectable in the room to generate a feature vector that is inputted to a classifier. The wireless signals may originate either in the room or out of the room. The feature vector contains wireless-signal characteristics measured by the mobile device (e.g., time-averaged power, Fourier coefficients) that are essentially unique to a room. The classifier, which may be an artificial neural network, predicts the occupancy of the mobile device in the room from the feature vector. Alternatively, the location of the mobile device in the room may be verified, wherein the feature vector may form a training example to train the classifier.

An environmental information acquisition method is performed by an environmental information acquisition apparatus that acquires a measurement result of a radio wave environment. The environmental information acquisition method includes distributing software of each radio scheme to one or more base station apparatuses measuring a radio wave environment of the radio scheme based on the software of the radio scheme, instructing the one or more base station apparatuses to measure the radio wave environment, and acquiring the measurement result of the radio wave environment from the one or more base station apparatuses measuring based on the software the radio wave environment in multiple sections set across an area.

The present disclosure is related to reconfigurable radio equipment and edge computing, and in particular, to technologies for cyber security and radio equipment supporting certain features ensuring protection from fraud, and testing interfaces related to reconfigurable radio equipment. Other embodiments may be described and/or claimed.

There are provided methods for predicting timing advance (TA) with respect to a base station. According to some embodiments, the method includes determining, by a user equipment (UE), a set of TAs, each TA corresponding to a particular distance from the base station and measuring, by the UE, a set of instances of a power metric, each instance of the power metric associated with a respective distance from the base station. The method further includes determining, by the UE, a set of differences between each of the instances of the power metric and determining, by the UE, a new TA at least in part using the set of TAs and the set of differences.

Systems and methods for autonomous signal modulation format identification are disclosed. In an example embodiment of the disclosed technology, a method includes applying higher-order statistics to an input signal to identify the input signal's modulation format. The method may include applying higher-order statistics to the input signal to calculate higher-order cumulant values for the input signal as higher-order cumulants are indicative of a particular modulation format signature. The method may further include employing a decision tree to determine the modulation format of the input signal.