Abstract
The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.
Highlights
The recent advent of heterogeneous services and use cases with diverse requirements have made modern wireless communication networks highly complex systems, which need to be carefully designed and operated to offer immersive experience to their customers, while keeping both capital expenditure (CapEx) and operational expenditure (OpEx) low.The increasing complexity of wireless ecosystems makes network planning, optimisation, and orchestration arduous tasks
We have seen in the previous section that the disaggregation between hardware and software along with programmability using the principles of SDN/network function virtualisation (NFV) are crucial for adopting a service-based architecture (SBA) in the 5G core network, and subsequently integrating big data analytics and intelligent control
The ongoing deployments of 5G wireless networks mainly support enhanced mobile broadband (eMBB) services, but it is envisaged that 5G/6G networks would penetrate across various vertical industries and offer many more customised services and applications
Summary
The recent advent of heterogeneous services and use cases with diverse requirements have made modern wireless communication networks highly complex systems, which need to be carefully designed and operated to offer immersive experience to their customers, while keeping both capital expenditure (CapEx) and operational expenditure (OpEx) low. New paradigms, ushered in by the recent developments in the field of artificial intelligence (AI), and, most data-driven machine learning (ML) techniques, offer new possibilities in operating complex systems, such as modern communication networks. The aspiration is that a massive amount of data, which is within our reach to collect and process within practical timeframes, can help alleviate the need for tedious mathematical modelling and simulations for operating complex systems. Motivated by the aforementioned potentials, this article aims to provide the interested readers with a comprehensive analysis of the most recent progress in the use of data-driven and ML-based approaches in the study of modern communication systems and networks with a focus on the key, most recent publications, and on the developments in the industry, major research projects and potential for new research presented by the gaps in the literature
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