Full-Duplex Millimeter Wave MIMO Channel Estimation: A Neural Network Approach
CoRR(2024)
摘要
Millimeter wave (mmWave) multiple-input-multi-output (MIMO) is now a reality
with great potential for further improvement. We study full-duplex
transmissions as an effective way to improve mmWave MIMO systems. Compared to
half-duplex systems, full-duplex transmissions may offer higher data rates and
lower latency. However, full-duplex transmission is hindered by
self-interference (SI) at the receive antennas, and SI channel estimation
becomes a crucial step to make the full-duplex systems feasible. In this paper,
we address the problem of channel estimation in full-duplex mmWave MIMO systems
using neural networks (NNs). Our approach involves sharing pilot resources
between user equipments (UEs) and transmit antennas at the base station (BS),
aiming to reduce the pilot overhead in full-duplex systems and to achieve a
comparable level to that of a half-duplex system. Additionally, in the case of
separate antenna configurations in a full-duplex BS, providing channel
estimates of transmit antenna (TX) arrays to the downlink UEs poses another
challenge, as the TX arrays are not capable of receiving pilot signals. To
address this, we employ an NN to map the channel from the downlink UEs to the
receive antenna (RX) arrays to the channel from the TX arrays to the downlink
UEs. We further elaborate on how NNs perform the estimation with different
architectures, (e.g., different numbers of hidden layers), the introduction of
non-linear distortion (e.g., with a 1-bit analog-to-digital converter (ADC)),
and different channel conditions (e.g., low-correlated and high-correlated
channels). Our work provides novel insights into NN-based channel estimators.
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