Abstract:

In order to correctly identify the channel coding parameters of low-density parity-check coded wireless communication systems with high-order modulation over correlated noise, two novel deep learning-based semi-blind identification algorithms for code rates are proposed under the assumption that the candidate set of code rates and the corresponding parity check matrices are known. The proposed neural networks consist of the denoising subnetwork and the code-rate identification subnetwork. Moreover, the denoising subnetwork designs the real-valued denoising subnetwork and the complex-valued denoising subnetwork. Compared to the real-valued denoising subnetwork, the complex-valued denoising subnetwork has a better ability to process complex-valued signals with the cost of high complexity. Furthermore, in order to reduce the complexity of the complex-valued denoising subnetwork, a novel network compression algorithm based on network pruning technology is proposed. Simulation results show that, by using the novel multi-task learning strategy which jointly optimizes the denoising loss function and the loss function corresponding to code-rate identification, on one hand, the proposed neural networks have better identification performance comparing with the traditional algorithms with correlated noise, on the other hand, the identification algorithm based on complex-valued denoising subnetwork still outperforms the identification algorithm based on real-valued denoising subnetwork when its complexity approaches to that of the identification algorithm based on real-valued denoising subnetwork by using the network compression algorithm.

Key words: correlated noise, blind identification of channel coding, low-density parity-check (LDPC) code, deep learning, complex-valued network