A Minorization-Maximization Method for Optimizing Sum Rate in the Downlink of Non-Orthogonal Multiple Access Systems

By Muhammad Fainan Hanif, Zhiguo Ding, T. Ratnarajah, George K. Karagiannidis. IEEE Transactions on Signal Processing, 2015

391 citations recorded by the source corpus.

Non-orthogonal multiple access (NOMA) systems have the potential to deliver higher system throughput, compared with contemporary orthogonal multiple access techniques. For a linearly precoded multiple-input single-output (MISO) system, we study the downlink sum rate maximization problem, when the NOMA principle is applied. Being a non-convex and intractable optimization problem, we resort to approximate it with a minorization-maximization algorithm (MMA), which is a widely used tool in statistics. In each step of the MMA, we solve a second-order cone program, such that the feasibility set in each step contains that of the previous one, and is always guaranteed to be a subset of the feasibility set of the original problem. It should be noted that the algorithm takes a few iterations to converge. Furthermore, we study the conditions under which the achievable rates maximization can be further simplified to a low complexity design problem, and we compute the probability of occurrence of this event. Numerical examples are conducted to show a comparison of the proposed approach against conventional multiple access systems.

Subfields: Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5dc111a3299ab5363972270052e7921fd94a9767

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Citations in the corpus

A Simple Design of IRS-NOMA Transmission (2019, background): “serving 2K users and these beams have to be non-orthogonal if 2K > M [19].”
A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends (2017, methodology): “The scheme proposed in [45] is a representative example for this approach, where users with larger distances from the”
Rate-splitting multiple access for downlink communication systems: bridging, generalizing, and outperforming SDMA and NOMA (2017, methodology): “In [14], the problem (5) with equal weights is solved by the approximation technique minorization-maximization algorithm (MMA).”
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, methodology): “that although beamformer-based MIMO is inspired by SDMA, it is applicable to both underloaded/critically loaded and overloaded regimes [25], [44].”
Optimal Joint Power and Subcarrier Allocation for Full-Duplex Multicarrier Non-Orthogonal Multiple Access Systems (2016, methodology): “A suboptimal joint power allocation and precoding design was presented in [10] for the maximization of the system throughput in single-carrier MIMO-NOMA systems.”
A Survey of Rate-Optimal Power Domain NOMA With Enabling Technologies of Future Wireless Networks (2019, methodology): “If the full CSIT is available, the design objective is to maximize one of the overall sum-rate [154], [160], [171], weighted sum-rate [170], [172], or, the worst user-rate [156], [169].”
Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization (2019, methodology): “2) Studies on Multiple-Input Multiple-Output (MIMO)-NOMA Systems: Hanif et al. [20] proposed an effective beamforming design algorithm to maximize the system sum rate, where a multi-antenna base station (BS) served multiple single-antenna users through the NOMA protocol.”
Millimeter Wave Communication: A Comprehensive Survey (2018, methodology): “Different from system throughput improvements based on NOMA, Hanif et al. [111] focus on downlink sum rate optimization by applying NOMA in multiple-input single-output (MISO) systems.”