Interference Cancelation in Massive MIMO Wireless Communication Systems Using the Kronecker Product–Constrained Least-Mean-Square Algorithm and Adaptive Kalman Filtering

A wireless communication system relies on wireless technology to transmit and receive information over a distance without the need for physical connections such as cables or wires. However, interference poses a significant challenge in such systems, occurring when multiple transmitters and receivers operate in the same frequency band. This interference hampers communication efficiency and necessitates sophisticated signal-processing techniques for mitigation. Interference cancelation techniques, particularly in Massive MIMO wireless communication systems, play a crucial role in reducing the impact of interference caused by multiple transmissions from different users. This paper proposes the utilization of the Kronecker product-constrained least-mean-square (KCLMS) algorithm to nullify interference and employs the adaptive Kalman filtering approach to further minimize interference effects. Simulation results demonstrate that the proposed KCLMS + adaptive Kalman method achieves superior performance, with an RMSE of 0.045, PCC of 0.980, and SIR of 32.0 dB, outperforming transformer, DNN, CNN-LSTM, and traditional filtering methods. Additionally, it exhibits high performance-to-cost efficiency with moderate computational time (0.84 ms per iteration) and memory usage (13.5 MB), making it a practical solution for efficient, interference-resilient wireless communications. Performance validation is conducted using subjective tests, with evaluation metrics like Pearson correlation coefficient (PCC) and root mean square error (RMSE) to demonstrate the improved outcome. The proposed methodology aims to be beneficial for wireless networks providing communication services.