Soft-Output Detection Methods for Sparse Millimeter-Wave MIMO Systems With Low-Precision ADCs

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 e...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 67; no. 4; pp. 2822 - 2836
Main Authors: Jeon, Yo-Seb, Do, Heedong, Hong, Song-Nam, Lee, Namyoon
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Analog to digital conversion
Analog to digital converters
Antenna arrays
Channel models
Complexity
Computational complexity
Computational efficiency
Computer simulation
Equalization
Error detection
Iterative methods
low-precision analog-to-digital converter (ADC)
Millimeter wave communications
Millimeter waves
MIMO (control systems)
MIMO communication
multiple-input-multiple-output (MIMO)
Optimization
Quantization (signal)
Radio frequency
Receiving antennas
soft-output detection
time-domain equalization
ISSN:0090-6778, 1558-0857
Online Access:Get full text
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Summary: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.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2019.2892048