Recurrent Network with Attention for Symbol Detection in Communication Systems
Kim Chia, Vishnu Monn Baskaran, KokSheik Wong, Moh Lim Sim, Chong Hin Chee · 2022
One major challenge for wireless receivers to maintain information fidelity involves the demodulation of faded signals in noisy environments. Typical demodulation techniques for M-ary quadrature amplitude modulated (M-QAM) signal utilize variants of coherent demodulation. This paper explores deep learning (DL), specifically by using a proposed architecture recurrent-attention networks to compliment, or even overcome the limitations of demodulating M-QAM symbols. The proposed model is shown to outperform the benchmark coherent demodulator and other DL-based demodulators such as convolutional neural network (CNN), recurrent neural network (RNN) and the hybrid of both up to 5 dB learning gain at a lower model complexity and requires less memory usage.