Deep learning for signal detection in NOMA systems

These files are to implement the deep learning method for signal detection in a two-user non-orthogonal multiple access (NOMA) system.

These files are to implement the deep learning method for signal detection in a two-user non-orthogonal multiple access (NOMA) system [1]. The 3 main scripts are to generate training data, to train the neural network and to produce testing results, respectively. The neural network is trained for a static scalar channel with phase fading and is used to detect transmitted symbols on a single subcarrier for 2 users simultaneously in a NOMA system. Two scenarios are considered and tested: one is with fewer number of pilot symbols and the other is with shorter length of cyclic prefix. The deep learning method is shown to be more robust than conventional channel estimation methods in both cases. For more information, please refer to [1].

[1] Narengerile and J. Thompson, "Deep Learning for Signal Detection in Non-Orthogonal Multiple Access Wireless Systems," 2019 UK/ China Emerging Technologies (UCET), Glasgow, United Kingdom, 2019, pp. 1-4.