IEEE Globecom Communications Conference,
Dec. 9-13, 2019, Waikoloa, HI, USA.
Robust Learning-Based ML Detection for Massive MIMO Systems with One-Bit Quantized Signals
Jinseok Choi (1),
Yunseong Cho (1),
Brian L. Evans (1), and
Alan Gatherer (2)
(1) Department of Electrical and Computer Engineering,
Wireless Networking and Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA
(2) Huawei Technologies, Plano, Texas USA.
Multiantenna Communications Project
In this paper, we investigate learning-based maximum likelihood (ML)
detection for uplink massive multiple-input and multiple-output (MIMO)
systems with one-bit analog-to-digital converters (ADCs).
To resolve large dependency of learning-base detection on the training
length, we propose two learning-based one-bit ML detection methods:
a biased-learning method and a dithering-and-learning method.
The biased-learning method keeps likelihood functions with zero probability
from wiping out the obtained information through learning, thereby providing
more robust detection performance.
Extending the biased method to the system with the knowledge of
signal-to-noise ratio, the dithering-and-learning method estimates more
likelihood functions by adding dithering noise to the quantization input.
The proposed methods are further improved by adopting post likelihood
function update, which exploits correctly decoded data symbols as training
Simulation results validate the detection performance of the proposed
methods in symbol error rate.
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