BNNs- and TISTA- Based Signature Code Design for User Identification and Channel Estimation over Multiple-Access Channel with Rayleigh Fading

Abstract

User identification (UI) and channel estimation (CE) are essential in wireless networks with numerous users. Signature-code-based UI and CE schemes are widely used owing to their high spectral efficiency. Traditional signature code uses a discrete sensing matrix as a dictionary to generate codewords. Subsequently, the sparse vector recovery algorithm is used to recover the user state and channel state information in the received signal to complete the UI and CE. In this study, we propose an end-to-end machine-learning-aided signature code scheme under a multiple-access Rayleigh fading channel called machine-learning signature code (ML-SC). The ML-SC consists of a binarized-neural-networks-based (BNNs-based) trainable encoder and a trainable-iterative-soft-threshold-algorithm-based (TISTA-based) trainable decoder. To improve the accuracy, the dictionary is optimized by minimizing the mean squared error between the original and recovered information. Our proposed scheme achieved better performance and efficiency than the conventional schemes in the simulation. Moreover, it was confirmed that the dictionary generated by the ML-SC is suitable for various conventional decoders. Finally, by analyzing the results of the simulations, we found that ML-SC improves the restricted isometric constants and coherence of the dictionary.