Resource allocation optimization in multiuser OFDM relay-assisted underwater acoustic sensor networks

Resource allocation optimization (RAO) is a crucial design issue for providing green communications in the sixth generation (6G) of underwater acoustic (UWA) sensor networks (SNs). To have a suitable convergence speed in the machine learning (ML)-based algorithms used for finding the solution of the online RAO problems, the optimal or suboptimal solution of the offline form of the online problem should be utilized as the initial setting. Moreover, knowing the closed-form of the best initial setting in terms of the channel parameters leads to more efficiency in the ML-based algorithms from the robustness standpoint. In this paper, the authors formulate and solve two new offline RAO problems for joint relay selection and power allocation in the orthogonal frequency division multiplexing UWA cooperative communication systems. They first obtain a new formula for the signal to noise ratio (SNR) per-subcarrier in the cooperative UWA communication system with amplify and forward relaying including multiple users and multiple relays. In their analyses, unlike terrestrial channel and by considering the physical facts seen in the practical UWA channel, they assume non-white Gaussian noise along with the frequency-dependent pathloss for obtaining the SNR per-subcarrier function. Then, they use it in the definition of their RAO problems. Their proposed problems are non-convex and they present a promising method for converting them to the convex problems. In their problems, the objective function is the total power transmitted over the network. In addition, the sum-rate and probability of error are constrained to control the quality of service. Also, they derive some new closed-form formulas for reliable cooperation, relay selection, and power loading. Extensive simulation studies are carried out to assess the convergence, effectiveness, and robustness of the authors' proposed algorithms to the channel impairments for different conditions.


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  • Accession Number: 01886516
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 28 2023 4:57PM