Multiple Access Techniques for Intelligent and Multi-Functional 6G: Tutorial, Survey, and Outlook

TL;DR

This paper surveys the landscape of multiple access (MA) techniques for next-generation 6G networks, arguing that the traditional orthogonal/non-orthogonal dichotomy is insufficient for the diverse, multi-functional, AI-enabled ecosystem. It presents Rate-Splitting MA (RSMA) as a unifying framework that subsumes orthogonal, power-domain NOMA, SDMA, and multicasting, and introduces the ambitious concept of Universal MA (UMA) to unify across all resource dimensions, including code-domain schemes. The work then surveys AI-enabled MA (AI for MA and MA for AI), ISAC-oriented MA, and MA for emerging intelligent applications like semantic communications, Metaverse, VR, and RIS-assisted networks, while outlining a roadmap toward 6G standardization. Key contributions include a unified taxonomy of MA techniques, a critical assessment of RSMA’s unifying potential, and a forward-looking agenda on UMA, AI integration, and ISAC-enabled MA for future wireless systems. The paper emphasizes multi-functionality and network intelligence as central drivers for rethinking MA design and standardization in 6G and beyond, with RSMA positioned as a foundational step toward UMA-driven, universal access strategies.

Abstract

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions to serve multiple users/devices/machines/services, ideally in the most efficient way. Given the needs of multi-functional wireless networks for integrated communications, sensing, localization, computing, coupled with the surge of machine learning / artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this paper, we provide a tutorial, survey and outlook of past, emerging and future MA techniques and pay a particular attention to how wireless network intelligence and multi-functionality will lead to a re-thinking of those techniques. The paper starts with an overview of orthogonal, physical layer multicasting, space domain, power domain, ratesplitting, code domain MAs, and other domains, and highlight the importance of researching universal multiple access to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, receiver designs, user behavior predictions, and MA for AI such as federated learning/edge intelligence and over the air computation. We then discuss MA for network multi-functionality and the interplay between MA and integrated sensing, localization, and communications. We finish with studying MA for emerging intelligent applications before presenting a roadmap toward 6G standardization. We also point out numerous directions that are promising for future research.

Figures (23)

• Figure 1: Shrinking the knowledge tree of MA by unifying MA schemes as we move from leaves, to the branches, to the trunk. UMA, not yet found, would be the holy grail of MA scheme unification.
• Figure 2: Illustration of the downlink transmission frameworks for 1-layer RS, HRS, and GRS with $3$ users.
• Figure 3: Illustration of the two-user downlink transmission frameworks and beam shapes for multicasting, OMA, SDMA, PD-NOMA, and RSMA.
• Figure 4: Illustration of the two-user uplink transmission frameworks for OMA, NOMA, and RSMA.
• Figure 5: Illustration of a SCMA system with 6 users and 4 resource blocks: (a) mapping between each user's two bits and SCMA codewords; (b) factor graph corresponding to the SCMA system.