A Survey of ML for the Physical Layer in 5G and Future Wireless Networks

Future wireless networks will be heavily dependent on 5th-generation technology (5G). The vast Internet of Everything (IoE), which will be powered by ground-breaking 5G network, will be able to service millions of network devices, users, and operations. Several use cases are among the 5G services that are made possible by the improved broadband access, large machine based communication, & best-consistent reduced communiqué. 5th generation networks may combine several systems into an unified platform, creating a landscape for seamless communication, especially for high-mobility applications. 5G systems, which have increased performance, are more susceptible to different research problems. In this context, we offer a thorough overview of 5G networks with an emphasis on artificial learning-based solutions that address present and upcoming problems. We begin by outlining the major performance parameters for the 5G network architecture in comparison to earlier and future network technologies. Second, we go over the features, uses, and use cases of next-generation wireless networks, which will enable quick communication to trillions of products. Next, we discuss the systems, activities, and problems related to the physical layer that reduces the signal strength. In addition, we provide research on 5G communication networks, 5G driving patterns, and 5G supporting designs. Additionally, we go through beam-forming methods to increase data speeds with effective spectrum access as well as signalling strategies for 5G huge multiple-input multiple-output systems (MIMO). We also look at privacy and security issues with 5G and practical advice from standard bodies for decision-makers. Furthermore, we also talk about new problems and solutions.