Fast Adaptive Minorization-Maximization Procedure for Beamforming Design of Downlink NOMA Systems

We develop a novel technique to accelerate minorization-maximization (MM) procedure for the non-orthogonal multiple access (NOMA) weighted sum rate maximization problem. Specifically, we exploit the Lipschitz continuity of the gradient of the objective function to adaptively update the MM algorithm. With fewer additional analysis variables and low complexity second-order cone program (SOCP) to solve in each iteration of the MM algorithm, the proposed approach converges quickly at a small computational cost. By numerical simulation results, our algorithm is shown to greatly outperform known solutions in terms of achieved sum rates and computational complexity.

Authors:
Lyons Oisin, Hanif Muhammad Fainan, Juntti Markku, Tran Le-Nam

Publication type:
A1 Journal article – refereed

Place of publication:

Keywords:
5G, algorithm acceleration, majorization-minimization, NOMA

Published:
11 May 2020

Full citation:
O. Lyons, M. F. Hanif, M. Juntti and L. Tran, “Fast Adaptive Minorization-Maximization Procedure for Beamforming Design of Downlink NOMA Systems,” in IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 8023-8027, July 2020, doi: 10.1109/TVT.2020.2993987

DOI:
https://doi.org/10.1109/TVT.2020.2993987

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020080448047