A Comprehensive Review on OFDM, 5G and Various PAPR Minimization Techniques based on Machine Learning

Abstract

The use of modulation methods in Fifth Generation (5G) wireless communication systems is essential for fulfilling the requirements of increased data rates, reduced latency, and enhanced connection. This entails the optimization of power spectral density (PSD), the improvement of data transmission reliability, and the reduction of bit error rates (BER) and interference. Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technique used in 4G and 5G wireless communication networks. OFDM results in a significant Peak to Average Power Ratio (PAPR) in the time domain because of the constructive interference between many subcarriers. This leads to increased complexity and expense of amplifiers, as well as higher costs and complexity of networks. Hence, it is essential to devise novel approaches to mitigate the PAPR in OFDM systems. ML has become a potential approach for addressing PAPR concerns. This study provides a thorough examination of ways for optimizing PAPR, with a specific emphasis on ML approaches.