Adaptive Relay Selection and Power Allocation for OFDM Cooperative Underwater Acoustic Systems

Doosti-Aref, Abdollah; Ebrahimzadeh, Ataollah

doi:10.1109/tmc.2017.2701820

Cited by 50 publications

(24 citation statements)

References 28 publications

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“…The noise constant level 0 is taken to be 50 dB for the quiet deep sea. The target BER is set to be 1 × 10 −3 , which can basically meet the data transmission requirement [27]. For the cognitive system, the number of the SU receivers (i.e., ) is set to be 4.…”

Section: Simulation and Analysismentioning

confidence: 99%

Adaptive Energy Efficiency Maximization for Cognitive Underwater Acoustic Network under Spectrum Sensing Errors and CSI Uncertainties

Yao

2019

Wireless Communications and Mobile Computing

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Energy efficiency (EE) maximization problem for Cognitive Underwater Acoustic Network is investigated in this study. Available works on EE usually assume that spectrum sensing is accurate or that channel state information (CSI) is perfect, which is often impractical. Thus, an adaptive resource allocation scheme is proposed to maximize the EE, subject to the transmission power constraint of secondary user (SU) and the interference power constraint of primary user (PU). By taking the spectrum sensing errors into account, we add power interference from PU to SU in the objective function. Besides, interference tolerance factor is introduced to control the interference from SU to PU. Assuming CSI uncertainties of the involved channels are bounded, they are separately modeled as stochastic-case or worst-case according to their nature. Since the established optimization problem is nonconvex, it is converted into a convex one and then solved by the techniques of fractional programming and dual decomposition. Simulation results validate that the EE can be improved by classifying the CSI uncertainties and solving the expectation of the CSI correlation function. Furthermore, the interference from SU to PU can be controlled well by the adjustment of the interference tolerance factor.

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Section: Simulation and Analysismentioning

confidence: 99%

Adaptive Energy Efficiency Maximization for Cognitive Underwater Acoustic Network under Spectrum Sensing Errors and CSI Uncertainties

Yao

2019

Wireless Communications and Mobile Computing

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“…Relays' Total Power Constraint. According to [19,21], it is necessary to consider the total power consumed by the system. In our condition, we first consider the total power of all the relays.…”

Section: Algorithm 2: E Optimization Algorithm Under Allmentioning

confidence: 99%

Outage Analysis and Power Allocation Optimization for Multiple Energy-Harvesting Relay System Using SWIPT

Liu

Zhu

Wang

2018

Mobile Information Systems

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Energy harvesting (EH) combined with cooperative relying plays a promising role in future wireless communication systems. We consider a wireless multiple EH relay system. All relays are assumed to be EH nodes with simultaneous wireless and information transfer (SWIPT) capabilities, which means the relays are wirelessly powered by harvesting energy from the received signal. Each EH node separates the input RF signal into two parts which are, respectively, for EH and information transmission using the power splitting (PS) protocol. In this paper, a closed-form outage probability expression is derived for the cooperative relaying system based on the characteristic function of the system’s probability density function (PDF) with only one relay. With the approximation of the outage probability expression, three optimization problems are built to minimize the outage probability under different constraints. We use the Lagrange method and Karush–Kuhn–Tucker (KKT) condition to solve the optimization problems to jointly optimize the relay’s PS factors and the transmit power. Numerical results show that our derived expression of the outage probability is accuracy and gives insights into the effect of various system parameters on the performance of protocols. Meanwhile, compared with the no optimal condition, our proposed optimization algorithms can all offer superior performance under different system constraints.

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“…Given that the harsh underwater environment, the algorithm cannot be directly applied to UASNs. In Reference [ 12 ], Doosti et al proposed an adaptive relay selection and power allocation algorithm for UASNs to improve capacity and reduce bit error rate based on perfect CSI. In this algorithm, the optimal relay was first selected, and then the optimal power allocation problem was solved.…”

Section: Introductionmentioning

confidence: 99%

Deep Q-Network-Based Cooperative Transmission Joint Strategy Optimization Algorithm for Energy Harvesting-Powered Underwater Acoustic Sensor Networks

Han

2020

Sensors

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Cooperative transmission is a promising technology for underwater acoustic sensor networks (UASNs) to ensure the effective collection of underwater information. In this paper, we study the joint relay selection and power allocation problem to maximize the cumulative quality of information transmission in energy harvesting-powered UASNs (EH-UASNs). First, we formulate the process of cooperative transmission with joint strategy optimization as a Markov decision process model. In the proposed model, an effective state expression is presented to better reveal interactive relationship between learning and environment, thereby improving the learning ability. Then, we further propose a novel reward function which can guide nodes to adjust power strategy adaptively to balance instantaneous capacity and long-term quality of service (QoS) under the dynamic unpredictable energy harvesting. More specifically, we propose a deep Q-network-based resource allocation algorithm for EH-UASNs to solve the complex coupled strategy optimization problem without any prior underwater environment information. Finally, simulation results verify the superior performance of the proposed algorithm in improving the cumulative network capacity and reducing outages.

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Adaptive Relay Selection and Power Allocation for OFDM Cooperative Underwater Acoustic Systems

Cited by 50 publications

References 28 publications

Adaptive Energy Efficiency Maximization for Cognitive Underwater Acoustic Network under Spectrum Sensing Errors and CSI Uncertainties

Adaptive Energy Efficiency Maximization for Cognitive Underwater Acoustic Network under Spectrum Sensing Errors and CSI Uncertainties

Outage Analysis and Power Allocation Optimization for Multiple Energy-Harvesting Relay System Using SWIPT

Deep Q-Network-Based Cooperative Transmission Joint Strategy Optimization Algorithm for Energy Harvesting-Powered Underwater Acoustic Sensor Networks

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