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Joint Beamforming and Deployment Optimization for UAV-Assisted Maritime Monitoring Networks

EasyChair Preprint no. 10268

12 pagesDate: May 26, 2023


With the wide application of Internet of Things (IoT) systems in smart ocean, many unmanned surface vehicles (USVs) have been deployed jointly with unmanned aerial vehicles (UA Vs) to monitor the maritime environment. However, conventional means of maritime communications fail to provide high rate services due to the complex mar- itime channel conditions and large transmission distance, which will affect the environmental monitoring performance. In this paper, we propose a USV-UA V collaborative patrol scheme for maritime environment monitoring networks. Considering the characteristic of energy concentration in beamforming, we investigate the joint beamforming and location deployment optimization problem (BLDO) for UA V relay. Specifically, we decompose the BLDO problem into two subproblems. In the first subproblem, the location deployment of UAV and beam gain allocation are optimized via an iterative algorithm based on the approximated beam patterns. The algorithm can effectively reduce the computational complexity of the grid-search method. In the second sub-problem, beamforming optimization is conducted with high-dimensional constant-modulus (CM) constraint. A micro-particle swarm optimization based algorithm with boundary relaxation (BR−µP SO) is proposed to obtain an optimal solution. Finally, the simulation results demonstrate that the proposed algorithms can improve the performance in terms of the achievable sum rate and the beam gain.

Keyphrases: Beamforming, Deployment, Maritime environment monitoring, UAV, USV

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Lin Liu and Bin Lin and Ran Zhang and Yudi Che and Chaoyue Zhang},
  title = {Joint Beamforming and Deployment Optimization for UAV-Assisted Maritime Monitoring Networks},
  howpublished = {EasyChair Preprint no. 10268},

  year = {EasyChair, 2023}}
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