Article: Deep learning-aided wireless channel estimation in 6G Journal: International Journal of Mobile Network Design and Innovation (IJMNDI) 2025 Vol.11 No.4 pp.236 - 247 Abstract: Various processes can facilitate the exchange of information, allowing multiple inputs and outputs. A base station (BS) is essential for a user terminal (UT) to obtain accurate channel state information (CSI). However, pilot signals can only be transmitted a limited number of times and for a specific duration. Due to the dynamic nature of the environment, the position of the UT is constantly changing. Consequently, the number of pilot signals remains constant, necessitating repeated transmission of pilot signals in neighbouring cells. This repetition results in pilot contamination, which complicates the establishment of connections between adjacent cells. The minimal mean square error (MMSE) method can still yield reasonable results, even in the presence of pilot contamination. While the BS has knowledge of the channels linked to each UT, this information is not available to the MMSE method. In this study, two channel estimation approaches are proposed. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning-aided wireless channel estimation in 6G

Authors: M. Sivanathan; A. Rajasekar; T. Veena; S. Aswini

Addresses: Information Technology Department, V.S.B. Engineering College, NH-67, Covai Road, Karudayampalayam Post, Karur, 639111, Tamil Nadu, India ' Department of Artificial Intelligence and Data Science, Sri Sairam Institute of Technology, Sai Leo Nagar, West Tambaram, Chennai, 600044, Tamil Nadu, India ' Department of Information Technology, S.A. Engineering College, Chennai, 600077, Tamil Nadu, India ' Department of Computer Science and Engineering, Bangalore Technological Institute, Bengaluru, Karnataka, India

Abstract: Various processes can facilitate the exchange of information, allowing multiple inputs and outputs. A base station (BS) is essential for a user terminal (UT) to obtain accurate channel state information (CSI). However, pilot signals can only be transmitted a limited number of times and for a specific duration. Due to the dynamic nature of the environment, the position of the UT is constantly changing. Consequently, the number of pilot signals remains constant, necessitating repeated transmission of pilot signals in neighbouring cells. This repetition results in pilot contamination, which complicates the establishment of connections between adjacent cells. The minimal mean square error (MMSE) method can still yield reasonable results, even in the presence of pilot contamination. While the BS has knowledge of the channels linked to each UT, this information is not available to the MMSE method. In this study, two channel estimation approaches are proposed.

Keywords: deep learning; CSI; channel state information; LS and MMSE; LoS; line-of-sight; IoT; Internet of Things; SNR; signal-to-noise ratio; MMPGA-LS; res net.

DOI: 10.1504/IJMNDI.2025.151018

International Journal of Mobile Network Design and Innovation, 2025 Vol.11 No.4, pp.236 - 247

Received: 25 Feb 2025
Accepted: 07 May 2025
Published online: 09 Jan 2026 *

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Title: Deep learning-aided wireless channel estimation in 6G

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