Work

Power Allocation in Orthogonal Frequency Division Multiplexing-based Wireless Networks Using Reinforcement Learning

Year: 2024

Type: article

Abstract: Energy efficiency is one of the most essential requirements for wireless sensor networks and the internet of things, since batteries are usually the main source of power for these networks. Appropriate techniques based on artificial intelligence can reduce power consumption and provide the required quality of service parameters for the network. In this paper, we approach the challenging problem of allocation of signal transmission power based on the orthogonal frequency division multiplexing technique. We propose to use reinforcement learning-based algorithms to find the optimal policy for allocating power to wireless network devices using a reward function. More specifically, we propose to use the double deep Q-network (DDQN) agent due to its higher learning capacity compared to the Q-learning, deep Q-network (DQN), and the distributed algorithm (DA), a classic algorithm in the literature for power allocation. Simulation results show that the DDQN agent presents promising solutions for power allocation in wireless networks.

Source: Electronic System of Publishing Periodicals (Federal University of Rio Grande do Sul)

DOI: 10.22456/2175-2745.130630

Authors: Lopes, Hudson Henrique de Souza, Vieira, Flávio Henrique Teles

Institution: Universidade Federal de Goiás

Language: English

Topic: Advanced Wireless Network Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Fundação de Amparo à Pesquisa do Estado de Goiás

OpenAlex ID: W6907704796

FWCI:

Cites:

Cited by:

Related to: