Machine Learning for Advanced Wireless Communication
Wasswa Shafik · 2024
The rapid evolution of wireless communication technologies has ushered in an era of unprecedented connectivity and data transfer capabilities. As the demand for more reliable, efficient, and intelligent wireless systems continues to grow, machine learning (ML) has emerged as a critical enabler in enhancing the performance of wireless communication networks. This chapter presents a literature overview on applying ML techniques to advanced wireless communication. The chapter commences with an overview of the role of ML in the field of wireless communication, highlighting the potential benefits and challenges. Then, it delves into various studies that explore the use of ML for signal-processing tasks such as signal denoising, equalization, and modulation recognition. Review research is done on channel estimation, elucidating how ML models effectively estimate and predict channel characteristics. The chapter further examines ML applications in interference management and spectrum sensing, where ML algorithms adaptively mitigate interference and optimize spectrum utilization. The chapter discusses utilizing ML techniques in resource allocation and beamforming, showcasing their capability to optimize resource allocation based on dynamic network conditions. Deep Learning (DL) and neural network architectures are also explored in wireless communication, highlighting their potential for handling complex tasks in this domain. A comparative analysis of the different ML techniques applied in wireless communication is presented, offering a critical assessment of their strengths and weaknesses. Ultimately, this chapter underscores the significance of ML in advancing wireless communication technologies, provides valuable guidance for future research in this exciting field, and shares some lessons learned.