Cognitive Non-Orthogonal Multiple Access with Cooperative Relaying: A New Wireless Frontier for 5G Spectrum Sharing

Lu Lv, Jian Chen, Qiang Ni, Zhiguo Ding, Hai Jiang

IEEE Communications Magazine · 2018 · 308 citations · 15 references

Concepts

TL;DR

Non‑orthogonal multiple access and cognitive radio are emerging 5G technologies that promise more efficient spectrum use and meet 5G goals of high efficiency, massive connectivity, low latency, and fairness. The article investigates integrating NOMA with cognitive radio into a cognitive NOMA network for intelligent spectrum sharing and discusses open challenges and future research directions. The study presents three cognitive NOMA architectures—underlay, overlay, and CR‑inspired—and proposes cooperative relaying strategies to mitigate inter‑ and intra‑network interference. The proposed cooperative relaying strategy significantly reduces outage probabilities across all cognitive NOMA architectures.

Abstract

Two emerging technologies toward 5G wireless networks, namely non-orthogonal multiple access (NOMA) and cognitive radio (CR), will provide more efficient utilization of wireless spectrum in the future. In this article, we investigate the integration of NOMA with CR into a holistic system, namely a cognitive NOMA network, for more intelligent spectrum sharing. Design principles of cognitive NOMA networks are perfectly aligned to functionality requirements of 5G wireless networks, such as high spectrum efficiency, massive connectivity, low latency, and better fairness. Three different cognitive NOMA architectures are presented, including underlay NOMA networks, overlay NOMA networks, and CR-inspired NOMA networks. To address inter-network and intra-network interference, which largely degrade the performance of cognitive NOMA networks, cooperative relaying strategies are proposed. For each cognitive NOMA architecture, our proposed cooperative relaying strategy shows its potential to significantly lower outage probabilities. We discuss open challenges and future research directions on implementation of cognitive NOMA networks.

References

15