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<p>Massive Multiple Input Multiple Output (MIMO) communication system is created for the fifth-generation (5G) network to improve spectral efficiency and transmission reliability. However, accurate channel estimation remains a critical challenge in massive MIMO systems, particularly due to issues such as pilot contamination and the high-dimensional beamforming complexity associated with large antenna arrays. Despite the recent development of effective estimating approaches, estimation accuracy still has to be increased. In order to estimate channels, this study presents an optimized LSTM system. The Least Square (LS) channel estimation approach is first used to gather historical data on the channel state information (CSI) derived from pilot sequences. These gathered channel answers are used to train the proposed LSTM, in which the squirrel search algorithm (SSA) is used for optimal weight initialization in the most effective manner. By optimizing the initial weights, SSA enhances convergence behavior and avoids poor local minima during training. The trained SSA-LSTM model is subsequently used to predict the current channel response. The recommended transmit-predict strategy's functionality is assessed by changing the pilot sequence. Bit Error Rate (BER) and Mean Square Error (MSE) as functions of Signal-to-Noise Ratio (SNR) were used to assess the performance of SSA-LSTM model. In order to compare various channel estimate methods, the studies altered the SNR (usually between 5 and 50&#xa0;dB), QAM modulation order (128, 256, 512, and 1024 QAM), and number of transmit and receive antennas (N<sub>t</sub> = N<sub>r</sub> = 100, 200, 300, and 400) under a Rayleigh fading channel model. The suggested SSA-LSTM model obtained a lower BER, which is a 27% improvement over the conventional LSTM approach at 20&#xa0;dB for N<sub>t</sub> = N<sub>r</sub> = 400, and a significantly lower MSE, which is a 33% improvement over the traditional LSTM at 20&#xa0;dB for N<sub>t</sub> = N<sub>r</sub> = 400. These results demonstrate that optimizing LSTM weights with the SSA is a highly effective strategy for improving channel estimation accuracy in Massive MIMO systems particularly under high-order QAM and large-scale antenna configurations relevant to 5G and emerging 6G networks.</p>

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Channel estimation in Rayleigh fading channel for next generation massive MIMO systems using weight optimized LSTM network

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Rajashree Suryawanshi,
  • B. P. Patil

摘要

Massive Multiple Input Multiple Output (MIMO) communication system is created for the fifth-generation (5G) network to improve spectral efficiency and transmission reliability. However, accurate channel estimation remains a critical challenge in massive MIMO systems, particularly due to issues such as pilot contamination and the high-dimensional beamforming complexity associated with large antenna arrays. Despite the recent development of effective estimating approaches, estimation accuracy still has to be increased. In order to estimate channels, this study presents an optimized LSTM system. The Least Square (LS) channel estimation approach is first used to gather historical data on the channel state information (CSI) derived from pilot sequences. These gathered channel answers are used to train the proposed LSTM, in which the squirrel search algorithm (SSA) is used for optimal weight initialization in the most effective manner. By optimizing the initial weights, SSA enhances convergence behavior and avoids poor local minima during training. The trained SSA-LSTM model is subsequently used to predict the current channel response. The recommended transmit-predict strategy's functionality is assessed by changing the pilot sequence. Bit Error Rate (BER) and Mean Square Error (MSE) as functions of Signal-to-Noise Ratio (SNR) were used to assess the performance of SSA-LSTM model. In order to compare various channel estimate methods, the studies altered the SNR (usually between 5 and 50 dB), QAM modulation order (128, 256, 512, and 1024 QAM), and number of transmit and receive antennas (Nt = Nr = 100, 200, 300, and 400) under a Rayleigh fading channel model. The suggested SSA-LSTM model obtained a lower BER, which is a 27% improvement over the conventional LSTM approach at 20 dB for Nt = Nr = 400, and a significantly lower MSE, which is a 33% improvement over the traditional LSTM at 20 dB for Nt = Nr = 400. These results demonstrate that optimizing LSTM weights with the SSA is a highly effective strategy for improving channel estimation accuracy in Massive MIMO systems particularly under high-order QAM and large-scale antenna configurations relevant to 5G and emerging 6G networks.