Deep Learning-Based Joint Channel Estimation and Equalization for IRS-Assisted Multi-Hop Wireless Networks - Charpe Prasanjeet Prabhakar; Moti Ranjan Tandi (Electronics, Communications, and Computing Summit, 2025)
In this paper, the new deep learning framework of joint channel estimation and signal equalization in Intelligent Reflecting Surface (IRS) assisted multi-hop wireless networks is proposed. IRS technology has also demonstrated an immense capability to improve signal propagation dynamic modification of the wireless environment especially in some complex multi-hop environments where conventional communication links can experience intense attenuation and fading. Nonetheless, the cascaded and dynamic IRS-assisted links come with much difficulty in the accurate estimation of channels and remedying inter-hop distortion. To fix this we would present an integrated model where Convolutional Neural Networks (CNNs) are used to extract spatial information and Bidirectional Long Short-Term Memory (Bi-LSTM) networks to represent temporal dependencies over multiple hops. The channel estimation is combined with signal equalization as end-to-end training of the framework is undertaken, thus removing the necessity to process sequentially. Simulations with an immense range of Rayleigh fading, different signal-to-noise ratio (SNR) and frequency conditions, prove that the proposed model provides great improvement in Bit Error Rate (BER) and Normalized Mean Square Error (NMSE) in comparison to conventional MMSE and LS-based techniques. These findings show that the deep learning is able to handle complex, high-dimensional interactions such as those used in IRS-enhanced multi-hop environments with promising efficiencies, pointing to low power consumptions and high throughputs in the next generation 6G and beyond systems.