Unmanned aerial vehicle cluster network communication method

Abstract

The invention provides an unmanned aerial vehicle cluster network communication method. The method comprises the steps of obtaining an initial value of an optimization variable of a to-be-solved optimization problem in an unmanned aerial vehicle cluster network based on non-orthogonal multiple access communication; and according to a preset convex optimization problem solving tool and a target constraint condition followed by the optimization variable of the to-be-solved optimization problem, optimizing the optimization variable of the to-be-solved optimization problem by adopting an iterative method so as to obtain a minimum user rate and an optimal power distribution scheme for averaging small-scale distribution in the unmanned aerial vehicle cluster network. Compared with an existing traditional OMA scheme and an NOMA scheme for optimizing power distribution based on known complete channel information of a transmitting end, the unmanned aerial vehicle cluster network communication method provided by the invention can effectively improve the average minimum user rate, so that the throughput rate of an unmanned aerial vehicle communication system is remarkably improved, and efficient multiple access of the unmanned aerial vehicle communication system is realized.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a communication method for a UAV cluster network. Background technique [0002] To achieve wide-area coverage with a UAV communication system, it is necessary to consider selecting an appropriate multiple access method. In wide-area communication systems, users are sparsely distributed, so the channel fading levels between users are quite different. If traditional Orthogonal Multiple Access (OMA), such as Time Division Multiple Access (TDMA) and Orthogonal Frequency Division Multiple Access (OFDMA), etc. are used, the throughput of the system will be limited by the maximum channel fading under the premise of ensuring user fairness. The user's transmission rate is limited, and the utilization rate of channel resources is low. [0003] In the NOMA UAV communication system, in order to maximize the user's minimum transmission rate, it is necessary to optimize the user's pow...

AI Technical Summary

Problems solved by technology

One is that most of the current work considers the power allocation problem of a single UAV, but in a multi-UAV system, each UAV only covers part of the users, thus simplifying the problem of multiple single UAVs. The related research on the power allocation problem under UAV cooperative transmission; secondly, most of the current research work assumes that the complete channel information is known at the sender when optimizing power allocation. However, in actual situations, the UAV and the user’s The location may change rapidly, and it is difficult to obtain complete channel information in real time

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