Abstract

Multiple-inputmultiple-output (MIMO) sparse code multiple access (SCMA) is of great interest for future wireless networks to achieve higher spectral efficiency and support massive connectivity. In this paper, we investigate the key problems of user clustering and downlink beamforming for MIMO-SCMA in a cloud radio access network (C-RAN). Using channel state information available at the central processor, an efficient user clustering algorithm based on the constrained $K$ -means method is proposed. Subsequently, two iterative algorithms for beamforming design are developed by minimizing the total transmission power under quality-of-service (QoS) and fronthaul capacity constraints. In the first approach, we approximate the continuous non-convex constraints by convex conic ones using first-order Taylor expansion and iteratively solve a sequence of mixed-integer second order cone programs (MI-SOCPs) to achieve high quality solution, but with higher complexity. In the second approach, a two-stage low-complexity solution is developed in which beamforming matrices obtained from each stage are combined to form a single beamformer for each user. In the first stage, cluster beamformers are designed by taking advantage of block diagonalization, while in the second stage, user-specific beamformers are determined by minimizing transmission power. The performance of the proposed user clustering and downlink beamforming approaches for MIMO-SCMA in C-RAN is validated through simulations over mmWave channels. Compared to benchmark approaches, the results show significant improvements in terms of transmit power and spectral efficiency.

Highlights

  • I N mobile wireless networks, multiple access technologies are of crucial importance to meet performance requirements in terms of data throughput, network capacity, device connectivity and energy consumption

  • Power domain non-orthogonal multiple access (NOMA) relies on the use of superposition coding strategies, wherein user signals are simultaneously broadcast with different power levels at the transmitter, while successive interference cancellation (SIC) techniques are employed to separate them at the receiver

  • 4) We evaluate the performance of the proposed algorithms for user clustering and downlink beamforming using in-depth simulations of Multiple-input multiple-output (MIMO)-sparse code multiple access (SCMA) in cloud radio access network (C-RAN) with mmWave channel models and different parameter configurations

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Summary

Introduction

I N mobile wireless networks, multiple access technologies are of crucial importance to meet performance requirements in terms of data throughput, network capacity, device connectivity and energy consumption. NOMA allows multiple users to access overlapping time and frequency resource elements in the same spatial layer [1]. This technology has the potential to provide higher spectral efficiency and meet the massive connectivity demand needed for machine-to-machine (M2M) communications and internet of things (IoT) in future wireless networks [2]. In code domain NOMA, different codes are applied to modulate the data streams of the users over multiple resource elements in a sparse manner. Power domain NOMA relies on the use of superposition coding strategies, wherein user signals are simultaneously broadcast with different power levels at the transmitter, while successive interference cancellation (SIC) techniques are employed to separate them at the receiver. In multiple domain NOMA, such as pattern division multiple access (PDMA) and lattice partition multiple access

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