Design of Relay Processing Matrix in Two-Way Relay Systems

In two-way relay communication scenarios, the design of the relay processing matrix plays a critical role in determining overall system performance. To optimize performance, we conducted comprehensive simulations of DPC-ZF (Dirty Paper Coding with Zero Forcing) and DPC-MMSE (Dirty Paper Coding with Minimum Mean Square Error) algorithms, implementing them using matrix operations and precoding techniques in MATLAB. Our comparative analysis revealed that these advanced algorithms significantly outperform traditional AF (Amplify-and-Forward) and ZF (Zero Forcing) approaches. The key advantage lies in their enhanced ability to handle channel noise through sophisticated preprocessing techniques, where DPC-ZF employs interference cancellation before ZF processing, while DPC-MMSE combines interference pre-subtraction with optimal linear filtering. We evaluated various relay processing matrix design methodologies, including code implementations for covariance matrix optimization and singular value decomposition-based approaches. The simulation framework incorporated channel state information feedback mechanisms and power allocation algorithms to assess performance under different SNR conditions. Our investigation demonstrates that in specific scenarios, particularly those with asymmetric channel conditions and interference constraints, the relay processing matrix design substantially impacts communication system performance metrics such as bit error rate and capacity. Therefore, in practical applications, greater emphasis should be placed on the design and optimization of relay processing matrices, incorporating adaptive algorithms that can dynamically adjust to changing channel conditions through real-time matrix recomputation and parameter tuning.