Interference Management for a Wireless Communication Network Using a Recurrent Neural Network Approach

Mohammad Abrar Shakil Sejan, Md Habibur Rahman, Md Abdul Aziz, Rana Tabassum, Young-Hwan You, Duck-Dong Hwang and Hyoung-Kyu Song ()
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Mohammad Abrar Shakil Sejan: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Md Habibur Rahman: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Md Abdul Aziz: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Rana Tabassum: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Young-Hwan You: Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
Duck-Dong Hwang: Department of Electronics and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Hyoung-Kyu Song: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2024, vol. 12, issue 11, 1-17

Abstract: Wireless communication technologies have profoundly impacted the interconnectivity of mobile users and terminals. Nevertheless, the exponential increase in the number of users poses significant challenges, particularly in interference management, which is a major concern in wireless communication. Machine learning (ML) approaches have emerged as powerful tools for solving various problems in this domain. However, existing studies have not fully addressed the problem of interference management for wireless communication using ML techniques. In this paper, we explore the application of recurrent neural network (RNN) approaches to address co-channel interference in wireless communication. Specifically, we investigate the effectiveness of long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent unit (GRU) network architectures in two different network settings. The first network comprises 10 connected devices, while the second network involves 20 devices. Our experimental results demonstrate that Bi-LSTM outperforms LSTM and GRU in terms of mean squared error, normalized mean squared error, and sum rate. While LSTM and GRU produce similar results, LSTM exhibits a marginal advantage over GRU. In addition, a combined RNN approach is also studied, and it can provide better results in dense networks.

Keywords: interference management; wireless network; deep learning; recurrent neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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