Deep Learning Channel Estimation for OFDM 5G Systems with Different Channel Models

Abstract

At cellular wireless communication systems, channel estimation (CE) is one of the key techniques that are used in Orthogonal Frequency Division Multiplexing modulation (OFDM). The most common methods are Decision‐Directed Channel Estimation, Pilot-Assisted Channel Estimation (PACE) and blind channel estimation. Among them, PACE is commonly used and has a steadier performance. Applying deep learning (DL) methods in CE is getting increasing interest of researchers during the past 3 years. The main objective of this paper is to assess the efficiency of DL-based CE compared to the conventional PACE techniques, such as least-square (LS) and minimum mean-square error (MMSE) estimators. A simulation environment to evaluate OFDM performance at different channel models has been used. A DL process that estimates the channel from training data is also employed to get the estimated impulse response of the channel. Two channel models have been used in the comparison: Tapped Delay Line and Clustered Delay Line channel models. The performance is evaluated under different parameters including number of pilots (64 pilots or 8 pilots), number of subcarriers (64), the length of cyclic prefix (16 or 0 samples) and carrier frequency (4 GHz) through computer simulation using MATLAB. From the simulation results, the trained DL estimator provides better results in estimating the channel and detecting the transmitted symbols compared to LS and MMSE estimators although, the complexity of the proposed LSTM estimator exceeds the equivalent LS estimator. Furthermore, the DL estimator also demonstrates its effectiveness with various pilot densities and with different cyclic prefix periods.

Implementation of the deep learning method for signal detection in massive-MIMO-NOMA systems

“…Convolutional layer insertion was followed by the application of dense layers with a 50 % dropout rate. Here is a mathematical representation of the functions of a DL-based detection system [ 41 ].…”

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“…However, the trade-off for fewer parameters with these models is the requirement for multiple iterations and message passing within the GNN. Other approaches have also been proposed utilizing Long-Short Term Memory (LSTM) models in [7], which show promising results in channel estimation and demodulation in Rician and 3GPP models.…”

Spatial Simultaneous Functioning-Based Joint Design of Communication and Sensing Systems in Wireless Channels

“…Furthermore, the investigations highlighted in [10,11] center around the channel estimation and signal detection within orthogonal frequency division multiplexing (OFDM) systems, leveraging a deep learning (DL) approach. This method is employed to comprehensively address wireless OFDM channels, from the estimation of channel state information (CSI) to symbol recovery.…”

“…SSF facilitates the concurrent operation of communication and sensing systems, thereby enhancing flexibility and efficiency, particularly in dynamic environments. While recent research efforts [7][8][9][10][11][12][13][14][15] have made significant strides in exploring joint communication and sensing scenarios, it is important to note that these studies primarily focus on the individual facets of the JCAS framework, such as waveform design, receiver architecture, and signal processing techniques. However, none of these studies consider SSF or compare it with TDMA.…”

“…In the context of JCAS, recent research efforts [7][8][9][10][11][12][13][14][15] have made notable progress. However, a discernible gap persists in addressing the key aspects comprehensively.…”