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

• Published in: Mathematics (Basel) 2024-06, Vol.12 (11), p.1755
• Main Authors: Sejan, Mohammad Abrar Shakil, Rahman, Md Habibur, Aziz, Md Abdul, Tabassum, Rana, You, Young-Hwan, Hwang, Duck-Dong, Song, Hyoung-Kyu
• Format: Article
• Language: English
• Subjects: Algorithms; Cochannel interference; Connectivity; Deep learning; Electromagnetic interference; Field programmable gate arrays; Global positioning systems; GPS; Graph representations; interference management; Internet of Things; Machine learning; Management; Memory; Mobile communication systems; Natural language processing; Network management systems; Neural networks; Performance evaluation; recurrent neural network; Recurrent neural networks; Signal processing; Spectrum allocation; Technology application; Time series; Voice recognition; Wireless communication systems; Wireless communications; wireless network; Wireless networks
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12111755

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.