Unmanned aerial vehicle cooperation and trajectory optimization method for throughput improvement

Abstract

The invention provides an unmanned aerial vehicle cooperation and trajectory optimization method for throughput improvement. The unmanned aerial vehicle cooperation and trajectory optimization methodis used for optimizing resource allocation of an unmanned aerial vehicle auxiliary cellular network. According to the method, one unmanned aerial vehicle flies periodically in the edge area of the cell, communication service is provided for users in the edge area of the cell, and the bandwidth allocation proportion of the unmanned aerial vehicle and the base stations, the service coverage radius of each base station and the flight path of the unmanned aerial vehicle are established and jointly optimized; and a target function for maximizing the minimum average throughput of the users in the edge area is decoupled into three sub-problems to be solved, so that a resource allocation result and an unmanned aerial vehicle flight path which meet requirements are obtained. According to the invention, the unmanned aerial vehicle trajectory can be adjusted according to the user density of each cell, balanced assistance for multiple cells is realized, the service quality of cells with dense users is improved, and the resource utilization efficiency is improved.

Description

technical field [0001] The present invention relates to the technical field of mobile communication, relates to the technology of UAV air base station auxiliary cellular network, and specifically relates to a method for UAV cooperation and trajectory optimization oriented to throughput improvement. Background technique [0002] In recent years, the application of mobile communication technology in daily life has become more and more extensive, and the data traffic has also increased at a relatively rapid rate. Faced with the surge of data traffic, traditional cellular networks have the following problems: First, GBS (ground base stations) in traditional cellular networks are usually deployed statically, making it difficult to flexibly handle the traffic offloading of edge users in communication hotspots Problems; secondly, the distance between the cell edge users and the ground base station in the hotspot area is relatively long, and the channel link status is poor; finally,...

AI Technical Summary

Problems solved by technology

Faced with the surge of data traffic, traditional cellular networks have the following problems: First, GBS (ground base stations) in traditional cellular networks are usually deployed statically, making it difficult to flexibly handle the traffic offloading of edge users in communication hotspots problems; secondly, the distance between cell edge users and ground base stations in hotspot areas is relatively long, and the channel link status is poor; finally, it is difficult for ground base stations to flexibly adapt to the scene of sudden traffic increase

However, in the prior art, there are few studies on considering multiple cells and establishing cooperative division between multiple GBSs and UAV air base stations, which makes it impossible to fully utilize the auxiliary role of UAV air base stations and effectively improve Edge Region User Throughput

Moreover, in the existing research on UAV-assisted heterogeneous networks, UAVs are usually deployed on the edge of a single cell or multiple independent cells to improve the service quality of edge users, but in hot spots such as squares and stadiums, multiple The distribution of users in a cell is often uneven. The existing UAV-assisted cellular network strategy is difficult to balance each cell. In a cell with dense users, the quality of data transmission service is poor and the resource utilization rate is low.

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