Full-Duplex Millimeter Wave MIMO Channel Estimation: A Neural Network Approach
Journal article, 2024
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, (eg, different numbers of hidden layers), the introduction of non-linear distortion (eg, with a 1-bit analog-to-digital converter (ADC)), and different channel conditions (eg, low-correlated and high-correlated channels). Our work provides novel insights into NN-based channel estimators.
mmWave MIMO
Channel estimation
full-duplex
neural networks.
Author
Mehdi Sattari
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Hao Guo
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Deniz Gündüz
Imperial College London
Ashkan Panahi
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Tommy Svensson
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
IEEE Transactions on Machine Learning in Communications and Networking
2831-316x (eISSN)
Vol. 2Areas of Advance
Information and Communication Technology
Subject Categories
Electrical Engineering, Electronic Engineering, Information Engineering
DOI
10.1109/TMLCN.2024.3432865