A Study on Wireless Waveform Modification Architectures Most Applicable for Seamless Data Transmission in 5G NR Frequencies

Abstract

Several studies have been conducted on the successive modification of the wireless network waveform resources for advanced purposes. This is to adapt to the evolving needs of wireless resources in areas such as growing data rates, effective bandwidth utilization, and power efficiency, among many others. Various methods of wireless waveform modification (WWM) have been used for data transmission in wireless communication frequencies. The multicarrier orthogonal transmission has proven more acceptable because of its agility to maneuver over impairments and combat signal fading and interference deterioration. This research studied the most marketable categories of wireless multicarrier architectures (WMAs) used in fifth-generation (5G) new radio (NR) frequencies; these include precoding, pulse shaping, and sub-band filtering. The successes and drawbacks observed from newly assigned frequencies for 5G NR also called for advanced modification of these existing waveform architectures. In view of this, the review suggested the inculcation of artificial intelligence (AI)-enabled machine learning (ML) algorithms for advanced waveform modifications to drive 5G-Advanced (5G-A), sixth generation (6G) network objectives, and beyond. This is to curtail the anomalies experienced by earlier architectures and pave a way for advanced utilization of wireless network resources.