Linear Large-Scale MIMO Data Detection for 5G Multi-Carrier Waveform Candidates

TL;DR

This paper explores the integration of large-scale MIMO with new multi-carrier waveforms, FBMC and GFDM, developing low-complexity detection algorithms and analyzing their performance, complexity, and PAPR in 5G systems.

Contribution

It introduces novel low-complexity data detection algorithms for FBMC and GFDM in large-scale MIMO, and evaluates their performance and trade-offs in 5G scenarios.

Findings

FBMC and GFDM reduce OOB emissions but increase computational complexity.

Higher PAPR observed with FBMC and GFDM compared to OFDM and SC-FDMA.

Trade-offs between spectral efficiency, complexity, and PAPR are characterized.

Abstract

Fifth generation (5G) wireless systems are expected to combine emerging transmission technologies, such as large-scale multiple-input multiple-output (MIMO) and non-orthogonal multi-carrier waveforms, to improve the spectral efficiency and to reduce out-of-band (OOB) emissions. This paper investigates the efficacy of two promising multi-carrier waveforms that reduce OOB emissions in combination with large-scale MIMO, namely filter bank multi-carrier (FBMC) and generalized frequency division multiplexing (GFDM). We develop novel, low-complexity data detection algorithms for both of these waveforms. We investigate the associated performance/complexity trade-offs in the context of large-scale MIMO, and we study the peak-to-average power ratio (PAPR). Our results show that reducing the OOB emissions with FBMC and GFDM leads to higher computational complexity and PAPR compared to that of orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency division multiple access (SC-FDMA).