Beijing University of Posts and Telecommunications

Wenjun Xu

Ping Zhang

Zhi Zhang

Hui Gao

Jiaru Lin

Zhiyong Feng

Fengyu Wang

Shaosheng Li

Chang Deng

Jinglin Zhang

Tingting Zou

Zhisong Bie

Chunlei Huang

Disclosed is a low-complexity linear equalization method for an Orthogonal Time Frequency Space (OTFS) system. The method may include: receiving a time domain signal passing through a linear time-varying (LTV) channel; sampling the time domain signal to obtain a sampled signal; demodulating the sampled signal to obtain a demodulated signal; performing a Symplectic Finite Fourier Transform (SFFT) on the demodulated signal to obtain a sampled delay-Doppler domain signal; determining an effective channel matrix in a delay-Doppler domain under a restriction of a rectangular window according to a time domain channel matrix; determining a linear equalization matrix according to the effective channel matrix; and equalizing the sampled delay-Doppler domain signal in a low-complexity way according to the linear equalization matrix. The disclosure also discloses a linear equalization device of an OTFS system for realizing the linear equalization method and a computer-readable storage medium.

Linear equalization method and device for OTFS systems

Disclosed is a method of path optimization for an unmanned aerial vehicle (UAV). In the method, users and the positions of the users are obtained; the users are divided into K wireless multicast user groups; K initial hovering positions corresponding to the K wireless multicast user groups are determined; and a first shortest flying path connecting the K initial hovering positions is determined; and the initial hovering positions of the UAV are optimized to obtain K final hovering positions and a final flying path. The present disclosure also discloses a device of path optimization for a UAV and a computer readable storage medium.

Method and device of path optimization for UAV, and storage medium thereof

Disclosed are methods for beam tracking. In the methods, a transmitting unmanned aerial vehicle (UAV) receives motion state information (MSI) of a receiving UAV and determines a first beam angle from the transmitting UAV to the receiving UAV according to the MSI of the receiving UAV. Then the transmitting UAV selects a codeword from a codebook according to the first beam angle. Finally, the transmitting UAV determines an activated sub-array and a beam-forming vector of the CCA according to the size of the activated sub-array corresponding to the sub-codebook to which the codeword belongs, a second beam angle corresponding to the codeword and a location of a central antenna element of an activated sub-array corresponding to the codeword and transmits information using the beam-forming vector. One or more examples of the present disclosure also provide a method for beam tracking implemented by the receiving UAV, methods for generating a codebook.

Methods for beam tracking and methods for generating a codebook

A method of route construction of an unmanned aerial vehicle (UAV) network includes: obtaining transmission information of the UAV network; determining a relay set from the UAV network based on the transmission information; wherein, the relay set includes at least one UAV; determining environment state parameters according to the transmission information and the information of the relay set; inputting the environment state parameters into a Deep Q-Learning network (DQN) to obtain an accumulated reward corresponding to each UAV; and selecting a UAV with the largest accumulated reward as a target UAV. This disclosure also discloses a UAV and computer readable storage medium that can be used to construct routes for a UAV network.

Method of route construction of UAV network, UAV and storage medium thereof

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Beijing, China

Wenjun Xu