An Adaptive Joint Filtering Approach to Wireless Relay Network for Transmission Rate Maximization

Abstract

This paper 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.
Simulation results demonstrate that the proposed adaptive joint scheme achieves 25–33% higher achievable rate than the conventional Fixed AF Relay and nearly double the throughput of a Direct Link transmission. The results validate that adaptive joint filtering provides superior spectral efficiency, improved ISI mitigation, and stable convergence, making it a practical and scalable solution for next-generation cooperative communication systems.