Low-Complexity FNN-Based Transmit Antenna Selection for Enhanced Performance in VASM Systems over Rician Fading Channels

  • Viet Vinh Tran Advanced Wireless Communications Group, Le Quy Don Technical University, Ha Noi, Viet Nam.
  • Thanh Hiep Pham Advanced Wireless Communications Group, Le Quy Don Technical University, Ha Noi, Viet Nam.
  • Thu Phương Nguyễn Advanced Wireless Communications Group, Le Quy Don Technical University, Ha Noi, Viet Nam
Keywords: Variable active antenna spatial modulation (VASM), antenna selection (TAS), feedforward neural network (FNN), Euclidean distance-based TAS (ED-TAS), Rician fading channel

Abstract

Variable Active Antenna Spatial Modulation (VASM) is a spatial modulation variant designed to improve
spectral efficiency and offer greater flexibility in system configuration. In this paper, we propose a feedforward neural network (FNN) framework to address the transmit antenna selection (TAS) problem, aiming to enhance performance in VASM systems operating over Rician fading channels. Computational results demonstrate that our novel FNN-TAS algorithm provides a significant reduction in computational costs compared to the standard Euclidean distance-based method. Additionally, simulation results indicate that the bit error rate (BER) of the VASM system increases with the Rician factor. However, the proposed FNN-TAS method effectively improves BER performance for VASM systems, particularly at high Rician factors, and outperforms conventional TAS methods based on channel gain.

Author Biographies

Viet Vinh Tran, Advanced Wireless Communications Group, Le Quy Don Technical University, Ha Noi, Viet Nam.

Tran Viet Vinh received a B.S. degree in Telecommunication Technological Command at Telecomunications University, Vietnam, in 2011, and an MS degree in Electronics Engineering from Le Quy Don Technical University, Vietnam, in 2017. He is currently studying for a Ph.D degree at Le Quy Don Technical University, Vietnam. His research interests include MIMO systems, spatial modulation, and antenna selection, deep learning for wireless communications.

Thanh Hiep Pham, Advanced Wireless Communications Group, Le Quy Don Technical University, Ha Noi, Viet Nam.

Pham Thanh Hiep received a B.E. degree in Communications Engineering from the National Defense Academy, Japan, in 2005; and received the M.E. and Ph.D. degrees in Physics, Electrical and Computer Engineering from Yokohama National University, Japan, in 2009 and 2012, respectively. He worked as an associate researcher at Yokohama National University, Yokohama, Japan, from 2012 to 2015. Now, he is a lecturer at Le Quy Don Technical University, Ha Noi, Vietnam. His research interests lie in the area of wireless communication technologies and signal processing.

Thu Phương Nguyễn, Advanced Wireless Communications Group, Le Quy Don Technical University, Ha Noi, Viet Nam

Nguyen Thu Phuong received the B.S, M.S and PhD degrees from Le Quy Don Technical University, Vietnam in 2008, 2012 and 2016, respectively. She is now a lecturer at Faculty of Radio-Electronics Engineering, and a significant member of the Advanced Wireless Communication Group, Le Quy Don Technical University, Hanoi,
Vietnam. Her research interests are in the area of emerging technologies for future wireless communications: Energy harvesting, Non-orthogonal multiple access (NOMA), space-time processing, space-time coding, spatial modulation and index modulation, and massive MIMO systems.

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Published
2025-02-24