Non-Orthogonal Multiple Access “NOMA” technique is expected to enhance the overall 6G performance. However some basic technologies are needed (i.e. Reconfigurable Intelligent Surfaces antenna technology as proposed by Emil Björnson and others) in order to further enhance the NOMA 6G user throughput. This article proposes some hints and directives on the necessary research to be further conducted.
Non-orthogonal multiple access (NOMA) presents interesting advantages and poses several demanding challenges when compared to the legacy OFDMA orthogonal multiple access (OMA) schemes. Although the 5G standards, at least in release Rel.15, have not approved the inclusion of NOMA techniques in the proposed 5G RAN solutions, in Rel.16 and subsequently in Rel.17 as well in 6G there is a preliminary proposal for such technology due to its advantages for high traffic load conditions in case of massive MTC or URLLC user equipment and services. In its fully deployed technical implementation NOMA promises an increased resource efficiency and scalability, along with lower latency than traditional OMA schemes. However the major disadvantage of NOMA will be the high requirements on proper channel estimation as well complex prediction and optimization algorithms including the Link Adaptation encoding and decoding mechanisms. The eventual downsized performance is reviewed against processing load with subsequent energy consumption and high demand on orthogonality among the non-orthogonal users.
From NOMA implementation point of view, several different technical approaches have been proposed throughout the previous years including among others the OFDMA with DL/UL power control, co-joint OFDMA CDMA, CoMP solutions including interference mitigation together with beam angular separation as part of MU-MIMO. Eventually NOMA by itself enhances the cell capacity by increasing the number of users but cannot support high throughput rates per user, an important demand in 6G. So the question remains, how will NOMA user throughput be boosted in 6G architectures? The answer to such a significant question could be reviewed by the contribution of three important technical contributions:
A. Cell-free solution: A macro-diversity or macro-MIMO or networked-MIMO multi-connectivity techniques in terms of base-station. In a strict definition Cell-free architectures are good candidates for 6G where groups of distributed massive MIMO panels are controlled by a central processor entity. By definition and following a good reference “J. Zhang, E. Bjornson, M. Matthaiou, D. W. K. Ng, H. Yang, and D. J. Love, “Prospective Multiple Antenna Technologies for Beyond 5G,” IEEE Journal on Selected Areas in Communications, vol. 8716, no. c, pp. 1–24, 2020” if the number of antennas in these elements is much greater than the number of users, then it is referred to as a cell-free massive MIMO network. The major benefit of such cell-free solution will be the outage probability reduction highly enhanced by the NOMA throughput performance. On the other hand the major drawback of such solution will be the newly introduced challenges in the design and deployment of radio access networks.
B. Machine Learning (ML) and Artificial Intelligence: The extensive use of Machine Learning (ML) algorithms for proper coding and Link Adaptation is the most common solution to decrease complexity and improve performance on the expense of processing load. The proposed in the international literature ML algorithms incorporate some enhancements on channel coding. Indeed 5G existing channel coding schemes like LDPC and Polar codes provide near-Shannon limit capacity error correction performance, however, when combined with high order modulation beyond 256 QAM research shows that there is more space for further improvements on capacity. A proposed joint probabilistic and geometric pulse shaping is used to design constellations with ‘auto-encoding’ to learn the constellation over a wide range of SNR including a joint frame error rate, data block size and transceiver NOMA power control and consumption. More than ever and due to the expected multi-service solutions, where the support of multiple types of codes results in excessive complexity and power consumption of the communication hardware, a unified coding solution approach is required with the joint consideration of the requirements of various service types, traffic models, data block lengths for optimum decoding approaches based on the expected amount of computing power available at the receiver, affordable latency and expected performance thresholds.
C. Reflective Intelligent Surfaces (RIS): RIS are expected to improve the massive MIMO beamforming and contribute further to the network deployment densification by reducing the interference. Following “Q. Wu et.al, Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial, IEEE Transactions on Communications ( Volume: 69, Issue: 5, May 2021)”, a new promising paradigm for 6G by leveraging a massive number of low-cost passive elements with independently controllable reflection amplitude and/or phase, named Intelligent Reflecting Surface (IRS), as illustrated in the following IEEE tutorial figure:
One of the major benefits of RIS antennas is flexible antenna implementation, cost-efficient alternative to improve outdoor and indoor coverage, however being passive elements rather than active they lack several functionalities when compared to traditional base stations. From the physical principles and the beamforming performance 6G requires a more comprehensive focus on medium access control (MAC) cross-layer functionality to transform RIS into a solid 6G RAN mMIMO antenna element. Following a good paper on RIS functionality “V. Croisfelt, F. Saggese, I. Leyva-Mayorga, R. Kotaba, G. Gradoni and P. Popovski, “A Random Access Protocol for RIS-Aided Wireless Communications,” in Proc. IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), 2022, pp. 1-5 “ it is more than evident that although the effort has already started there is still a long way before RIS functionality will be fully adopted in 6G architecture.