On the Designs and Challenges of Practical Binary Dirty Paper Coding

We propose a practical scheme for binary dirty-paper channels. By exploiting the concept of random binning instead of superposition coding, the complexity of the system is greatly reduced. For comparison, the existing approaches require one of the native codes to be of non-uniform a priori distribution, which is generally achieved by combining a symbol mapper and high-order-alphabet low-density parity-check (LDPC) codes. Using high-order alphabets increases significantly the complexity and the resulting method is not flexible for designing systems of practical channel parameters. In contrast, we propose to implement the random binning concept using only binary LDPC and binary convolutional codes. In this work, some design challenges of this random binning approach are identified and addressed. Our systems are optimized by the joint use of density evolution (DE) and the extrinsic information transfer (EXIT) analysis. Simulation results using practical Quasi-Cyclic LDPC codes show that our system achieves similar performance to the state-of-the-art, high-order-alphabet LDPC-based systems while demonstrating significant advantages in terms of complexity and flexibility of system design.