Linear Large-Scale MIMO Data Detection for 5G Multi-Carrier Waveform Candidates
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).
Cite
@article{arxiv.1512.00411,
title = {Linear Large-Scale MIMO Data Detection for 5G Multi-Carrier Waveform Candidates},
author = {Nihat Engin Tunali and Michael Wu and Chris Dick and Christoph Studer},
journal= {arXiv preprint arXiv:1512.00411},
year = {2015}
}
Comments
Presented at the Asilomar Conference on Signals, Systems, and Computers