Optimal Throughput Fairness Trade-offs for Downlink Non-Orthogonal Multiple Access over Fading Channels

Recently, non-orthogonal multiple access (NOMA) has attracted considerable interest as one of the 5G-defining techniques. However, as NOMA is intrinsically in favour of the transmission of strong users who are capable of carrying out successive decoding, judicious designs are required to guarantee user fairness. In this paper, a two-user downlink NOMA system over fading channels is considered. For delay-tolerant transmission, the average sum-rate is maximized subject to both average and peak power constraints as well as a minimum average user rate constraint. The optimal resource allocation is obtained using Lagrangian dual decomposition under full channel state information at the transmitter (CSIT), while an effective power allocation policy under partial CSIT is also developed based on analytical results. In parallel, for delay-limited transmission, the sum of delay-limited throughput (DLT) is maximized subject to a maximum allowable user outage constraint under full CSIT, and the analysis for the sum of DLT is performed under partial CSIT. Furthermore, a sophisticated orthogonal multiple access (OMA) scheme is also studied as a benchmark to prove the superiority of NOMA over OMA with full CSIT. Finally, the theoretical analysis is verified via simulations by means of various trade-offs for the average sum-rate (sum-DLT) versus the minimum (maximum) user rate (outage) requirement. Index Terms Non-orthogonal multiple access, orthogonal multiple access, fading channel, fairness, strong duality, dual decomposition Part of this paper has been submitted to the IEEE Wireless Communications and Networking Conference (WCNC), 2018. H. Xing, Y. Liu, and A. Nallanathan are with the Department of Informatics, King’s College London, London, WC2R 2LS, U.K. (e-mails: [email protected]; [email protected]; [email protected]). Z. Ding and H. V. Poor are with the Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA (e-mail: [email protected]). Z. Ding is also with the School of Computing and Communications, Lancaster University, Lancaster, LA1 4YW, U.K. (e-mail: [email protected]).