Modulation Classification Based on Fourth-Order Cumulants of Superposed Signal in NOMA Systems

In this paper, we study the automatic modulation classification in a non-orthogonal multiple access system. To mitigate the effect of interference, a likelihood-based algorithm and a fourth-order cumulant-based algorithm are proposed. Different from the maximum likelihood classifier for a single signal without interference, a likelihood function of the far and near users’ signals is derived. Then, a marginal probability for the far user is obtained by using the Bayesian formula. Hence, the modulation type can be determined by maximizing the marginal probability. The high computational complexity of the likelihood-based algorithm renders it impractical; accordingly, it serves as a theoretical performance bound. On the other hand, we construct a feature vector through the estimated fourth-order cumulants of the received signal including the superposed signal and noise. For each modulation pair, using the mean and covariance matrix of the estimated feature vector, its probability density function can be obtained. Then, the key is to calculate the mean and covariance matrix of the estimated feature vector. To solve this problem, the moments of the superposed signal are derived. Therefore, modulation classification can be performed by maximizing the probability density function. Extensive simulations verify that the two proposed algorithms perform well under a wide range of signal-to-noise ratios and observation lengths.

[1]  A. Nandi,et al.  Blind Modulation Classification for MIMO systems using Expectation Maximization , 2014, 2014 IEEE Military Communications Conference.

[2]  Hamid Sharif,et al.  Performance Evaluation of Feature-based Automatic Modulation Classification , 2018, 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS).

[3]  Yahia A. Eldemerdash,et al.  Signal Identification for Multiple-Antenna Wireless Systems: Achievements and Challenges , 2016, IEEE Communications Surveys & Tutorials.

[4]  Il-Min Kim,et al.  Likelihood-Based Modulation Classification for Multiple-Antenna Receiver , 2013, IEEE Transactions on Communications.

[5]  Mort Naraghi-Pour,et al.  Blind Modulation Classification over Fading Channels Using Expectation-Maximization , 2013, IEEE Communications Letters.

[6]  Octavia A. Dobre,et al.  Cyclostationarity-Based Robust Algorithms for QAM Signal Identification , 2012, IEEE Communications Letters.

[7]  Shu-Ming Tseng,et al.  Cross PHY/APP Layer User Grouping and Power Allocation for Uplink Multiantenna NOMA Video Communication Systems , 2020, IEEE Systems Journal.

[8]  Tamal Bose,et al.  Hierarchical Modulation Classification Using Deep Learning , 2018, MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM).

[9]  David S. Watkins,et al.  Fundamentals of matrix computations , 1991 .

[10]  H. Vincent Poor,et al.  Delay Minimization for NOMA-Assisted MEC Under Power and Energy Constraints , 2019, IEEE Wireless Communications Letters.

[11]  Zhiguo Ding,et al.  Beamforming Design and Power Allocation for Full-Duplex Non-Orthogonal Multiple Access Cognitive Relaying , 2018, IEEE Transactions on Communications.

[12]  Chrysostomos L. Nikias,et al.  Higher-order spectral analysis , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[13]  Shih-Chun Lin,et al.  Automatic Modulation Classification Under Non-Gaussian Noise: A Deep Residual Learning Approach , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[14]  Pramod K. Varshney,et al.  Artificial Neural Network Based Automatic Modulation Classification over a Software Defined Radio Testbed , 2018, 2018 IEEE International Conference on Communications (ICC).

[15]  Huiling Jiang,et al.  Receiver Design for Downlink Non-Orthogonal Multiple Access (NOMA) , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[16]  Rabah Attia,et al.  Identification of superposed modulations for two-way relaying MIMO systems with physical-layer network coding , 2017, IET Commun..

[17]  Tianshuang Qiu,et al.  Automatic Modulation Classification Using Cyclic Correntropy Spectrum in Impulsive Noise , 2019, IEEE Wireless Communications Letters.

[18]  Daejung Yoon,et al.  Blind Signal Classification for Non-Orthogonal Multiple Access in Vehicular Networks , 2018, IEEE Transactions on Vehicular Technology.

[19]  Sinan Gezici,et al.  Optimal Joint Modulation Classification and Symbol Decoding , 2019, IEEE Transactions on Wireless Communications.

[20]  H. Vincent Poor,et al.  A Fair Individual Rate Comparison between MIMO-NOMA and MIMO-OMA , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).

[21]  Asoke K. Nandi,et al.  Blind Digital Modulation Classification Using Minimum Distance Centroid Estimator and Non-Parametric Likelihood Function , 2014, IEEE Transactions on Wireless Communications.

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

[23]  Jae Hong Lee,et al.  Outage Probability for Cooperative NOMA Systems With Imperfect SIC in Cognitive Radio Networks , 2019, IEEE Communications Letters.

[24]  Saeed Hakimi,et al.  Optimized Distributed Automatic Modulation Classification in Wireless Sensor Networks Using Information Theoretic Measures , 2017, IEEE Sensors Journal.

[25]  Octavia A. Dobre,et al.  Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues , 2017, IEEE Wireless Communications.

[26]  Y. Bar-Ness,et al.  Higher-order cyclic cumulants for high order modulation classification , 2003, IEEE Military Communications Conference, 2003. MILCOM 2003..

[27]  Yiyang Pei,et al.  Robust Modulation Classification under Uncertain Noise Condition Using Recurrent Neural Network , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[28]  Mohamed Marey,et al.  Blind Modulation Classification Algorithm for Single and Multiple-Antenna Systems Over Frequency-Selective Channels , 2014, IEEE Signal Processing Letters.

[29]  Jianping Zheng,et al.  Likelihood-Based Automatic Modulation Classification in OFDM With Index Modulation , 2018, IEEE Transactions on Vehicular Technology.

[30]  Octavia A. Dobre,et al.  Low Complexity Automatic Modulation Classification Based on Order Statistics , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[31]  Zhiguo Ding,et al.  A General Power Allocation Scheme to Guarantee Quality of Service in Downlink and Uplink NOMA Systems , 2016, IEEE Transactions on Wireless Communications.

[32]  Brian M. Sadler,et al.  Hierarchical digital modulation classification using cumulants , 2000, IEEE Trans. Commun..

[33]  H. Block,et al.  A Multivariate Extension of Hoeffding's Lemma. , 1988 .

[34]  Asoke K. Nandi,et al.  Automatic Modulation Classification Using Combination of Genetic Programming and KNN , 2012, IEEE Transactions on Wireless Communications.

[35]  Jinjin Men,et al.  Performance Analysis of Nonorthogonal Multiple Access for Relaying Networks Over Nakagami-$m$ Fading Channels , 2017, IEEE Transactions on Vehicular Technology.

[36]  Mengüç Öner,et al.  Joint Space Time Block Code and Modulation Classification for MIMO Systems , 2017, IEEE Wireless Communications Letters.

[37]  Asoke K. Nandi,et al.  Modulation classification in MIMO fading channels via expectation maximization with non-data-aided initialization , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[38]  Shilian Wang,et al.  Continuous Phase Modulation Classification via Baum-Welch Algorithm , 2018, IEEE Communications Letters.

[39]  Octavia A. Dobre,et al.  On the likelihood-based approach to modulation classification , 2009, IEEE Transactions on Wireless Communications.

[40]  Jun Won Choi,et al.  Deep neural network-based automatic modulation classification technique , 2016, 2016 International Conference on Information and Communication Technology Convergence (ICTC).

[41]  Octavia A. Dobre,et al.  On the Cyclostationarity of OFDM and Single Carrier Linearly Digitally Modulated Signals in Time Dispersive Channels: Theoretical Developments and Application , 2010, IEEE Transactions on Wireless Communications.

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

[43]  Octavia A. Dobre,et al.  Second-Order Cyclostationarity of Mobile WiMAX and LTE OFDM Signals and Application to Spectrum Awareness in Cognitive Radio Systems , 2012, IEEE Journal of Selected Topics in Signal Processing.

[44]  Jun Won Choi,et al.  Deep neural network-based blind modulation classification for fading channels , 2017, 2017 International Conference on Information and Communication Technology Convergence (ICTC).

[45]  Ekram Hossain,et al.  Modeling and Analysis of Uplink Non-Orthogonal Multiple Access in Large-Scale Cellular Networks Using Poisson Cluster Processes , 2016, IEEE Transactions on Communications.

[46]  Octavia A. Dobre,et al.  A Low Complexity Modulation Classification Algorithm for MIMO Systems , 2013, IEEE Communications Letters.

[47]  H. Vincent Poor,et al.  Energy-Efficient Joint User-RB Association and Power Allocation for Uplink Hybrid NOMA-OMA , 2019, IEEE Internet of Things Journal.

[48]  D. Brillinger Time series - data analysis and theory , 1981, Classics in applied mathematics.