Robust Time-Invariant Filtering for Wireless Relay Optimization

This book presents the design, implementation, and performance evaluation of an Adaptive Joint SCAMP Filter and Relay Weight Optimization Scheme for a wireless Amplify-and-Forward (AF) cooperative relay network operating over frequency-selective fading channels. Conventional AF systems suffer from compounded noise and Inter-Symbol Interference (ISI) due to cascaded multi-tap channel effects. To address these limitations, this work employs a Joint Adaptive Filtering approach that simultaneously optimizes the source pre-coding filter and the relay amplification weight to minimize the end-to-end Mean Squared Error (MSE) and enhance the achievable data rate.The joint optimization problem is solved using the Projected Subgradient Method (PSGM), which provides robustness against non-linear constraints such as sparsity while maintaining low computational complexity. The algorithm is implemented and tested in a MATLAB simulation environment under a time-varying Auto-Regressive (AR(1)) fading model. Key performance metrics such as MSE convergence, filter characteristics, achievable rate, and robustness to parameter variations are analyzed.

Autorentext

Dr. Arjun Nichal is an Associate Professor at the Adarsh Institute of Technology and Research Centre, Vita, Maharashtra. He has published papers in international journals and conferences, holds multiple patents, and actively shares technical knowledge through his blogs and YouTube channels.