User Identification and Channel Estimation by DNN-Based Decoder on Multiple-Access Channel

The user identification scheme for a multiple-access fading channel based on the binary signature code is considered. In previous works, the signature code was used over a noisy multiple-access adder channel, and only the status of uses was decoded by the signature decoder. In this study, by considering the communication model as a compressed sensing process, it is possible to estimate the channel coefficients while identifying users. To improve the efficiency of the decoding process, we proposed an iterative deep neural network (DNN)-based decoder. Our simulation results show that for the binary signature code, our proposed DNN-based decoder requires less computing time to achieve higher active user detection accuracy and channel estimation accuracy than the classical signal recovery algorithm used in compressed sensing.