A study on the adoption of Wireless Communication in Big Data Analytics Using Neural Networks and Deep Learning

Abstract

The study explores about the Wireless communication which is one of the most rapidly evolving and active technology fields in the communication world. Wireless communication is a means of transmitting data from one point to another without the use of wires, cables, or another physical medium. As a result, commercial network operators have risen at an accelerating rate, bringing in the era of big data. Machine learning has been applied in a range of corporate and academic research contexts as one of the most promising artificial intelligences (AI) methods for deciphering this deluge of knowledge. This study presents a high-level introduction of big data handling and technological advances, as well as their potential applications in next-generation wireless networks (NG). Following that, we employ advanced analytics to estimate mobile users' demands and then use that knowledge to improve the efficacy of “community wireless communication channels.” In specifically, a unified, huge data-aided computer learning framework comprised of feature extraction, data modelling, and prediction/online refinement is provided. The primary benefits of the proposed framework are that we could create the logic, problem formulations, and method of powerful computational models inside the frame of wireless networks by depending on vast data that reflects the both spectrum and other hard demands of users. Via general, information is conveyed from sender to receiver across a certain distance in a communication system. The transmitter may be located anywhere within a few meters using Wireless Communication… We examined currently known approaches and explored their benefits and drawbacks in order to identify new research avenues for future advancements in underwater sensor networks.