Aspects of the subject disclosure may include, for example, obtaining, over an uplink (UL) using an aggregation of modular antenna arrays, a modulated signal that includes feedback transmitted by a user equipment (UE), wherein the aggregation of modular antenna arrays comprises multiple groups of antenna elements, after the obtaining the modulated signal, performing a demodulation of the modulated signal, determining demodulator constellation errors from the demodulation of the modulated signal, performing an error gradient weight adaptation responsive to the determining the demodulator constellation errors to derive revised weights for various antenna elements of the multiple groups of antenna elements, and applying the revised weights to the various antenna elements of the multiple groups of antenna elements to adjust signals received over the UL. Other embodiments are disclosed.

Provided is a channel identification method. The method includes: acquiring channel data of a terminal; constructing a first feature vector based on the channel data, where the first feature vector represents a numerical value set of cross-correlation values, which change along with time intervals, between channel data at different moments and the channel data themselves and between the channel data at different moments and subsequent channel data at different time intervals; and inputting the first feature vector into a predetermined prediction model to predict a speed of the terminal, or to predict a speed type of a cluster to which the terminal belongs. Further provided are an adaptive transmission method and apparatus based on the channel identification method, a transmission device, a base station, and a computer storage medium.

Facilitating a transmission power dependent resource reservation protocol in advanced networks (e.g., 5G, 6G, and beyond) is provided herein. Operations of a method can comprise defining, by a system comprising a memory and a processor, a resource reservation procedure that associates respective amounts of reserved resources available for the mobile device based on a transmission power level of the mobile device. The method also can comprise selecting, by the system, an amount of reserved resources from the respective amounts of reserved resources available based on the transmission power level of the mobile device.

The present technology is directed to visualizing a Wi-Fi signal propagation in 3-D based on a building plan defined with implicit geometry. The present technology can extract data from a building plan file where a 3-D space and objects in a building plan are defined with implicit geometry, transform the implicit geometry to explicit geometry for the 3-D space and the objects to translate a positioning of the 3-D space and objects from a local coordinate system to a global coordinate system, calculate a RF propagation pattern in the 3-D space based on a RF propagation model for a plurality of Wi-Fi access points located in the 3-D space, and present a 3-D visualization of the RF propagation pattern in the 3-D space.

A method of wireless communication by a network device includes receiving a location of a user equipment (UE). The method also includes receiving information associated with neighbor cell transmit beams. The method further includes predicting a spatial inter-cell interference-based distribution at the location of the UE based on the information associated with the neighbor cell transmit beams.

An apparatus, method and computer program for predicting a future quality of service of a wireless communication link based on a predicted future environmental model t predicted by determining a trajectory of active wireless transceivers. The method includes determining an environmental model of active transceivers in the environment of the mobile transceiver, determining information on a trajectory of the active transceivers, determining a predicted future environmental model of the active transceivers at a point in time of the future based on the information on the trajectory of the active transceivers, and predicting the future quality of service of the wireless communication link for the point in time of the future using a machine-learning model. The machine-learning model provides information on a predicted quality of service for a given environmental model. The predicted future environmental model is used as input to the machine-learning model.

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.

A detecting method for detecting a dynamic status in a space, wherein at least two wireless communication devices are deployed in the space and capable of performing a channel state detection to obtain a channel state information, the detecting method comprising: controlling the at least two wireless communication devices to perform the channel state detection in a registration stage to obtain a plurality of registration-stage channel state information; determining an environmental data of the space according to the plurality of registration-stage channel state information; controlling the at least two wireless communication devices to perform the channel state detection in a detection stage to obtain a plurality of detection-stage channel state information; and determining an intrusion situation of the space according to the environmental data and the plurality of detection-stage channel state information.

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.

An object of the present disclosure is to enable prediction of communication quality in accordance with wireless communication under frequency conditions as combinations of frequencies and frequency bandwidths.

The present disclosure provides a system including: an environment information generation unit configured to generate environment information of a terminal that performs wireless communication; a communication prediction model storage unit configured to store a plurality of communication prediction models obtained by learning relationships between the environment information and communication quality of wireless communication under frequency channel conditions; a communication prediction model generation unit configured to select one or more communication prediction models from among the plurality of communication prediction models and use the selected communication prediction models to generate a communication prediction model; and a communication prediction unit configured to input the environment information generated by the environment information generation unit to the communication prediction model generated by the communication prediction model generation unit and predict current or future communication quality of the terminal.