Multi-Node Joint Power Allocation Algorithm Based on Hierarchical Game Learning in Underwater Acoustic Sensor Networks

Author:

Wang HuiORCID,Huang Yao,Luo Fang,Yang Liejun

Abstract

In order to improve the overall service quality of the network and reduce the level of network interference, power allocation has become one of the research focuses in the field of underwater acoustic communication in recent years. Aiming at the issue of power allocation when channel information is difficult to obtain in complex underwater acoustic communication networks, a completely distributed game learning algorithm is proposed that does not require any prior channel information and direct information exchange between nodes. Specifically, the power allocation problem is constructed as a multi-node multi-armed bandit (MAB) game model. Then, considering nodes as agents and multi-node networks as multi-agent networks, a power allocation algorithm based on a softmax-greedy action selection strategy is proposed. In order to improve the learning efficiency of the agent, reduce the learning cost, and mine the historical reward information, a learning algorithm based on the two-layer hierarchical game learning (HGL) strategy is further proposed. Finally, the simulation results show that the algorithm not only shows good convergence speed and stability but also can adapt to a harsh and complex network environment and has a certain tolerance for incomplete channel information acquisition.

Funder

National Natural Science Foundation of China

Fujian Provincial Natural Science Fund

project of the Education Department of Fujian Province

principal fund project of Minnan Normal University

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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