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

Abstract

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, we 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. We 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 our analyses, unlike terrestrial channel and by considering the physical facts seen in the practical UWA channel, we assume non-white Gaussian noise along with the frequency-dependent pathloss for obtaining the SNR per-subcarrier function. Then, we use it in the definition of our RAO problems. Our proposed problems are non-convex and we present a promising method for converting them to the convex problems. In our 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, we 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 our proposed algorithms to the channel impairments for different conditions.