Deep Learning-Aided TR-UWB MIMO System

• Published in: IEEE transactions on communications Vol. 70; no. 10; pp. 6579 - 6588
• Main Authors: Zia, Muhammad Umer, Xiang, Wei, Huang, Tao, Haider Naqvi, Ijaz
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bit error rate; Channel estimation; Correlation; Data transmission; Deep learning; delay estimation; Delays; equalizers; Frequency division duplexing; Interference; Markov processes; MIMO communication; Multiple Input Multiple Output (MIMO) communications; Neural networks; Time-frequency analysis; Ultra-Wideband (UWB); Ultrawideband
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2022.3199489

This paper presents a novel deep learning-aided scheme dubbed <inline-formula> <tex-math notation="LaTeX">PR\rho </tex-math></inline-formula>-net for improving the bit error rate (BER) of the Time Reversal (TR) Ultra-Wideband (UWB) Multiple Input Multiple Output (MIMO) system with imperfect Channel State Information (CSI). The designed system employs Frequency Division Duplexing (FDD) with explicit feedback in a scenario where the CSI is subject to estimation and quantization errors. Imperfect CSI causes a drastic increase in BER of the FDD-based TR-UWB MIMO system, and we tackle this problem by proposing a novel neural network-aided design for the conventional precoder at the transmitter and equalizer at the receiver. A closed-form expression for the initial estimation of the channel correlation is derived by utilizing transmitted data in time-varying channel conditions modeled as a Markov process. Subsequently, a neural network-aided design is proposed to improve the initial estimate of channel correlation. An adaptive pilot transmission strategy for a more efficient data transmission is proposed that uses channel correlation information. The theoretical analysis of the model under the Gaussian assumptions is presented, and the results agree with the Monte-Carlo simulations. The simulation results indicate high performance gains when the suggested neural networks are used to combat the effect of channel imperfections.