A Survey on Non-Orthogonal Multiple Access with Energy Harvesting for Underwater Acoustic Sensor Networks

2021 ◽  
Author(s):  
Veerapu Goutham ◽  
T.N. Sreedhar Kumar ◽  
V.P. Harigovindan
Keyword(s):  
Sensor Networks ◽  
Energy Harvesting ◽  
Multiple Access ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks

scholarly journals Convex optimization–based multi-user detection in underwater acoustic sensor networks

2018 ◽  
Vol 14 (2) ◽  
pp. 155014771875766 ◽  
Author(s):  
Jianping Wang ◽  
Shujing Zhang ◽  
Wei Chen ◽  
Dechuan Kong ◽  
Zhou Yu
Keyword(s):  
Sensor Networks ◽  
Computational Complexity ◽  
Convex Optimization ◽  
Code Division Multiple Access ◽  
Multiple Access ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks ◽  
Code Division Multiple

Multi-carrier code-division multiple access is an important technical means for high-performance underwater acoustic sensor networks. Nevertheless, severe multiple access interference is a huge challenge. As the core technology, multi-user detection is used to eliminate multiple access interference. The traditional optimal detection algorithms (e.g. maximum likelihood) have very high computational complexity, and the performances of suboptimal detection methods (i.e. zero forcing, minimum mean square error, etc.) are poor. Therefore, taking into account the characteristics of underwater acoustic sensor networks, it is of great significance to design multi-user detection algorithms for achieving a tradeoff between the detection performance and the computational complexity in multi-carrier code-division multiple access systems. In this article, we design a transmitter model of underwater multi-carrier code-division multiple access system and then implement a multi-user detection algorithm based on convex optimization, which is named convex optimization–based algorithm. Next, we conduct the detection performance and computational complexity comparisons of maximum likelihood, zero forcing, minimum mean square error, and convex optimization–based algorithm. The results show that the performance of convex optimization–based algorithm is close to that of maximum likelihood, and the complexity is close to that of zero forcing. Therefore, a tradeoff between the computational complexity and the detection performance is realized in convex optimization–based algorithm. It means that convex optimization–based algorithm is suitable for the multi-user detection in multi-carrier code-division multiple access systems of underwater acoustic sensor networks.

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

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6519
Author(s):  
Song Han ◽  
Luo Li ◽  
Xinbin Li
Keyword(s):  
Sensor Networks ◽  
Energy Harvesting ◽  
Cooperative Transmission ◽  
Superior Performance ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks ◽  
Joint Strategy

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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