DEEP LEARNING BASED CHANNEL ESTIMATION FOR  5G AND BEYOND

ZAINAB SH.  HAMMED; SIDDEEQ Y.  AMEEN

doi:10.26682/csjuod.2023.26.2.46

ZAINAB SH. HAMMED *,**College of Engineering, University of Duhok, Kurdistan Region-raq
SIDDEEQ Y. AMEEN *Duhok Polytechnic University, Technical College of Engineering, Kurdistan Region-Iraq

DOI: https://doi.org/10.26682/csjuod.2023.26.2.46

Keywords: Deep learning; channel estimation; 5G; OFDM; MMSE; LS; and LSMT.

Abstract

As the demand for high-speed and reliable wireless communication continues to grow, 5G technology has emerged as a promising solution to meet these requirements. However, accurate channel estimation is essential for reliable and efficient data transmission in 5G and beyond. Traditional channel estimation techniques face challenges as a result of the time-varying nature of channels for wireless communication, frequency-selective fading, and interference from neighboring subcarriers. To address these challenges, deep learning models have emerged as promising solutions for channel estimation in 5G systems and beyond. Leveraging the powerful representation learning capabilities of neural networks approach have been adopted to learn the underlying channel characteristics directly from the received signals, without relying on explicit mathematical models. This approach offers several advantages, including improved estimation accuracy, reduced computational complexity, and enhanced robustness against channel variations. In this paper, a DL network for Long- Short- Term - Memory (LSTM) is utilized in channel estimation approach and compare the results with traditional approaches like Minimum- Mean- Square- Error (MMSE) and Least Squares (LS). Results demonstrate that deep learning models can achieve superior estimation accuracy, especially with low number of pilots leading to increased spectral efficiency, enhanced system capacity, and reduce the latency, even in challenging channel conditions, which is the main requirements in 5G and beyond.

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