Paper Info
Title
Soft-Output Detection Methods for Sparse Millimeter-Wave MIMO Systems With Low-Precision ADCs
Abstract
In this paper, we propose computationally efficient yet near-optimal soft-output detection methods for coded millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) systems with low-precision analog-to-digital converters (ADCs). The underlying idea of the proposed methods is to construct an extremely sparse inter-symbol-interference channel model by jointly exploiting the delay-domain sparsity in mmWave channels and a high quantization noise caused by low-precision ADCs. Then, we harness this sparse channel model to create a trellis diagram with a reduced number of states and a factor graph with very sparse edge connections, which are used for the computationally efficient soft-output detection methods. Using the reduced trellis diagram, we present a soft-output detection method that computes the log-likelihood ratios (LLRs) of coded bits by optimally combining the quantized received signals obtained from multiple receive antennas using a forward-and-backward algorithm. To reduce the computational complexity further, we also present a low-complexity detection method using the sparse factor graph to compute the LLRs in an iterative fashion based on a belief propagation algorithm. Simulations results demonstrate that the proposed soft-output detection methods provide significant frame-error-rates gains compared with the existing frequency-domain equalization techniques in a coded mmWave MIMO system using one- or two-bit ADCs.
Year
DOI
Venue
2019
10.1109/TCOMM.2019.2892048
IEEE Transactions on Communications
Keywords
DocType
Volume
MIMO communication,Channel models,Computational complexity,Quantization (signal),Antenna arrays,Receiving antennas,Radio frequency
Journal
67
Issue
ISSN
Citations 
4
0090-6778
2
PageRank 
References 
Authors
0.37
0
4
Name
Order
Citations
PageRank
Yo-Seb Jeon1549.12
Heedong Do241.08
Song-Nam Hong324433.84
Namyoon Lee485762.30