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) Futurewei 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 overcome the significant dependency of learning-based detection on the
training length, we propose two 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 a system with knowledge of the received
signal-to-noise ratio, the dithering-and-learning method estimates more
likelihood functions by adding dithering noise to the quantizer input.
The proposed methods are further improved by adopting the post likelihood
function update, which exploits correctly decoded data symbols as training
The proposed methods avoid the need for channel estimation.
Simulation results validate the detection performance of the proposed methods
in symbol error rate.
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Last Updated 08/25/19.