Abstract
Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks.
Highlights
Sixth Generation (6G) is a new wireless technology that many academics and researchers are embarking on
We provide an overview of broad categories of Machine Learning (ML) techniques including Deep Learning (DL) and their potential role in future 6G communication systems
The case study shows how smart biometric application works at application level and infrastructure level
Summary
Sixth Generation (6G) is a new wireless technology that many academics and researchers are embarking on. Enabling network nodes to make a decision based on local observation is highly preferable In such scenarios, if ML techniques are deployed at user level close to the edge of the network (Edge computing), the system can benefit from the nearby computations. The article undertakes the consideration to meet the network capacity demand, high efficiency, low latency, minimum processing time, the security of communication system by comparing the performance matrices like power allocation, resource management, caching, energy efficiency, etc. The authors deemed that the reality of 6G is ML and AI which need further investigation in layers of wireless communication model This includes understanding signal processing in the physical layer, data mining at the network, etc.
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