A Cooperative Positioning Method for Micro UAVs in Challenge Environment

He, Chenglong; Yu, Baoguo; Yi, Qingwu

doi:10.1109/icus50048.2020.9274889

Cited by 4 publications

(1 citation statement)

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“…The ISAC protocol, system architecture design [8], signal-sharing [8][9][10][11][12][13], time-sharing [14], array-sharing [15], spectrum-sharing [16] and power-sharing [17] have been studied to realize the ISAC system. In signal-sharing, due to no significant difference between radar and communication in the time and spatial domains, both the radar detection and the wireless communication use the same waveform [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System

et al. 2022

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Due to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share a radar module’s resources, coupled with efficient resource allocation methods. It can effectively solve the problem of inadequate UAV resources and the low utilization rate of resources. In this paper, the resource allocation problem is addressed for group UAVs to achieve a trade-off between the detection and communication performance, where the ISAC system is equipped in group UAVs. The resource allocation problem is described by an optimization problem, but with group UAVs, the problem is complex and cannot be solved efficiently. Compared with the traditional resource allocation scheme, which needs a lot of calculation or sample set problems, a novel reinforcement-learning-based method is proposed. We formulate a new reward function by combining mutual information (MI) and the communication rate (CR). The MI describes the radar detection performance, and the CR is for wireless communication. Simulation results show that compared with the traditional Kuhn Munkres (KM) or the deep neural network (DNN) methods, this method has better performance with the increase in problem complexity. Additionally, the execution time of this scheme is close to that of the DNN scheme, and it is better than the KM algorithm.

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