A Machine-Learning-Based Blind Detection on Interference Modulation Order in NOMA Systems

In order to blindly detect the modulation order of interference signals in downlink non-orthogonal multiple access systems, a machine learning (ML) algorithm based on Anderson–Darling test is proposed in this letter. The proposed algorithm adopts ML to determine the modulation order of interference user equipment from the raw received constellation points automatically. In feature extraction, a novel feature is introduced to improve the accuracy of blind detection. To evaluate the performance of blind detection, the detection rate and the throughput are simulated under different scenarios. Simulation results show that the proposed algorithm outperforms conventional algorithm on modulation order detection.

[1]  Zhiyong Feng,et al.  Cumulant based maximum likelihood classification for overlapped signals , 2016 .

[2]  George K. Karagiannidis,et al.  A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends , 2017, IEEE Journal on Selected Areas in Communications.

[3]  Ali Abdi,et al.  Survey of automatic modulation classification techniques: classical approaches and new trends , 2007, IET Commun..

[4]  Octavia A. Dobre,et al.  Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges , 2016, IEEE Communications Surveys & Tutorials.

[5]  Alireza Bayesteh,et al.  Blind detection of SCMA for uplink grant-free multiple-access , 2014, 2014 11th International Symposium on Wireless Communications Systems (ISWCS).

[6]  M. Stephens.  EDF Statistics for Goodness of Fit and Some Comparisons , 1974 .

[7]  Zhi Chen,et al.  Efficient Multi-User Detection for Uplink Grant-Free NOMA: Prior-Information Aided Adaptive Compressive Sensing Perspective , 2017, IEEE Journal on Selected Areas in Communications.

[8]  Heunchul Lee,et al.  Interference cancellation based on blindly-detected interference parameters for LTE-advanced UE , 2015, 2015 IEEE International Conference on Communications (ICC).

[9]  Jing Wang,et al.  Uplink Nonorthogonal Multiple Access in 5G Systems , 2016, IEEE Communications Letters.

[10]  H. Vincent Poor,et al.  Reinforcement Learning-Based NOMA Power Allocation in the Presence of Smart Jamming , 2018, IEEE Transactions on Vehicular Technology.