Artificial intelligence (AI) and machine learning (ML) for beyond 5G/6G communications

Abstract

Over the past few years, we have seen there is huge growth with new opportunities in using digital technologies for a wide range of services across modern society and across the economy which has influenced our daily life in some ways.Artificial intelligence and machine learning are the fastest growing and more demanding at the heart of those innovative digital technologies/services.The expectation from beyond 5G /6G communication systems is to provide services with higher system capacity, low latency, high reliability, greater spectral efficiency as well as enabling massive Internet of things (IoT).It will be very difficult to achieve these requirements without automation of the network systems.Therefore, all prospective uses of AI/ML must be taken into consideration during the design of future wireless networks in order to realize the vision of an intelligent network that will facilitate automation in network management and operations.The articles in this special issue highlight current research and development (R&D) trends and findings in design, management, and optimization of B5G/6G networks with the use of AI/ML.The article by Georgios P. Koudouridis et al. suggests a framework that offers three different AI architectural options: centralized, fully decentralized, and hybrid.The framework, in more detail, recognizes the logical AI functions, establishes how they map to the B5G radio access network architecture, and examines the deployment cost elements, notably the compute, communicate, and store costs.Based on a use case scenario for heterogeneous networks, the framework is assessed.It is demonstrated that the deployment cost profiling varies for the various AI architecture possibilities, and that this cost should be taken into account when deploying and choosing the AI/ML solution.The most promising concept in B5G seems to be cell-free massive MIMO (CF M-MIMO) technology.CF M-MIMO systems have some drawbacks despite their enticing features, including power distribution and channel estimation.In a variety of scientific fields, including wireless communications, deep learning (DL) has been effectively applied to a wide range of challenges.The article by Lazaros Alexios Iliadis et al. provides a review of the most recent DL techniques used in CF M-MIMO communication

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