Abstract
In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power throughout the frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. In comparison to recently published methods, our results show that the success rate improves. The suggested method directly senses the physical channel because it computes the SINR and codding rate of received signal just after the signal is detected by successive interference cancellation (SIC). Hence, because of this direct sense, this algorithm can really decrease occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity during the eNode B connections. Simulation results compared to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement.
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References
Feng, L., Yang, Z., Yang, Y., Que, X., & Zhang, K. (2020). Smart mode selection using online reinforcement learning for VR broadband broadcasting in D2D assisted 5G HetNets. IEEE Transactions on Broadcasting, 66(2), 600–611. https://doi.org/10.1109/TBC.2020.2977577
Qadir, J., Hussain, A., Yau, K. A., Imran, M. A., & Wolisz, A. (2018). Computational intelligence techniques for mobile network optimization [Guest Editorial]. IEEE Computational Intelligence Magazine, 13(1), 28–28. https://doi.org/10.1109/MCI.2017.2773799
Damnjanovic, A., Montojo, J., Wei, Y., Ji, T., Luo, T., Vajapeyam, M., Yoo, T., Song, O., & Malladi, D. (2011). A survey on 3GPP heterogeneous networks. IEEE Wireless Communications, 18(3), 10–21. https://doi.org/10.1109/MWC.2011.5876496
Naderpour, M., & Khaleghi Bizaki, H. (2020). Low overhead NOMA receiver with automatic modulation classification techniques. IET Communications, 14(5), 768–774. https://doi.org/10.1049/iet-com.2018.5099
Hermawan, A. P., Ginanjar, R. R., Kim, D., & Lee, J. (2020). CNN-based automatic modulation classification for beyond 5G communications. IEEE Communications Letters, 24(5), 1038–1041. https://doi.org/10.1109/LCOMM.2020.2970922
Jafar, N., Paeiz, A., & Farzaneh, A. (2021). Automatic modulation classification using modulation fingerprint extraction. Journal of Systems Engineering and Electronics, 32(4), 799–810. https://doi.org/10.23919/JSEE.2021.000069
Chen, W., Xie, Z., Ma, L., Liu, J., & Liang, X. (2019). A faster maximum-likelihood modulation classification in flat fading non-Gaussian channels. IEEE Communications Letters, 23(3), 454–457. https://doi.org/10.1109/LCOMM.2019.2894400
Nie, J., Zhang, Y., He, Z., Chen, S., Gong, S., & Zhang, W. (2019). Deep hierarchical network for automatic modulation classification. IEEE Access, 7, 94604–94613. https://doi.org/10.1109/ACCESS.2019.2928463
Jagannath J. et al. (2018). Artificial neural network based automatic modulation classification over a software defined radio testbed. In 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, 2018, pp. 1–6, doi: https://doi.org/10.1109/ICC.2018.8422346.
Norolahi, J., Mehrnia, M., and Azmi, P. (2022). Blind modulation classification via combined machine learning and signal feature extraction. In 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), 2022, pp. 266–271, doi: https://doi.org/10.1109/ISMODE53584.2022.9742733.
Gökçay, D., Eken, A., & Baltacı, S. (2019). Binary classification using neural and clinical features: an application in fibromyalgia with likelihood-based decision level fusion. IEEE Journal of Biomedical and Health Informatics, 23(4), 1490–1498.
Ghasemzadeh, P., Banerjee, S., Hempel, M., and Sharif, H. (2019). Accuracy analysis of feature-based automatic modulation classification with blind modulation detection. In 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, 2019, pp. 1000–1004.
Ali, A., and Yangyu, F. (2017). Automatic modulation classification using principle composition analysis based features selection. In 2017 Computing Conference, London, 2017, pp. 294-296. doi: https://doi.org/10.1109/SAI.2017.8252117.
Uys, L. Y., Gouws, M., Strydom, J. J., and Helberg, A. S. J., (2017). The performance of feature-based classification of digital modulations under varying SNR and fading channel conditions. In 2017 IEEE AFRICON, Cape Town, 2017, pp. 198-203.
Wang, F., & Wang, X. (2010). Fast and robust modulation classification via kolmogorov-smirnov test. IEEE Transactions on Communications, 58(8), 2324–2332.
Lee, J. H., Kim K. and Shin, Y. (2019). Feature image-based automatic modulation classification method using CNN algorithm. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan, 2019, pp. 1–4.
Gao, Z. and Zhu, Z. (2018). Distribution test based low complexity modulation classification in MIMO systems. In 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP), Hangzhou, 2018, pp. 1–5.
Han, L., Gao, F., Li, Z., & Dobre, O. A. (2017). Low complexity automatic modulation classification based on order-statistics. IEEE Transactions on Wireless Communications, 16(1), 400–411.
Azarmanesh, O. and Bilén, S. G., (2011). New results on a two-stage novel modulation classification technique for cognitive radio applications. In 2011 - MILCOM 2011 Military Communications Conference, Baltimore, MD, 2011, pp. 266-271.
Azarmanesh, O., & Bilén, S. G. (2013). I-Q diagram utilization in a novel modulation classification technique for cognitive radio applications. EURASIP Journal on Wireless Communications and Networking, 1, 2013.
Häring, L., Chen, Y., and Czylwik, A., (2010). Efficient modulation classification for adaptive wireless OFDM systems in TDD mode. In 2010 IEEE Wireless Communication and Networking Conference, Sydney, NSW, 2010, pp. 1-6.
Li, H., Bar-Ness, Y., Abdi, A., Somekh, O. S. and Su, W., (2006). OFDM modulation classification and parameters extraction. In 2006 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Mykonos Island, 2006, pp. 1–6.
Joshi, H. and Darak, S. J. (2017). Sub-Nyquist sampling and machine learning based online automatic modulation classifier for multi-carrier waveform. In 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), Montreal, QC, 2017, pp. 1-4.
Thakur, P. S., Madan S., and Madan, M. (2015). A novel method for automatic classification of multicarrier signals on noisy HF channel. In 2015 International Conference on Computer, Communication and Control (IC4), Indore, 2015, pp. 1–6.
Zhang, J., Li, Y., & Yin, J. (2018). Modulation classification method for frequency modulation signals based on the time–frequency distribution and CNN. IET Radar, Sonar & Navigation, 12(2), 244–249.
Zhu, Z., & Nandi, A. K. (2015). Automatic modulation classification: principles, algorithms and applications. Wiley Publishing.
Sills, J. A. Maximum-likelihood modulation classification for PSK/QAM. In MILCOM 1999. IEEE Military Communications. Conference Proceedings (Cat. No.99CH36341), Atlantic City, NJ, USA, 1999, pp. 217–220 vol.1, doi: https://doi.org/10.1109/MILCOM.1999.822675.
Polydoros, A., & Kim, K. (1990). On the detection and classification of quadrature digital modulations in broad-band noise. IEEE Transactions on Communications, 38(8), 1199–1211. https://doi.org/10.1109/26.58753
Huan, C.-Y., & Polydoros, A. (1995). Likelihood methods for MPSK modulation classification. IEEE Transactions on Communications, 43(2/3/4), 1493–1504. https://doi.org/10.1109/26.380199
Sapiano, P. C. and Martin, J. D. (1996). Maximum likelihood PSK classifier. In Proceedings of MILCOM '96 IEEE Military Communications Conference, McLean, VA, USA, 1996, pp. 1010–1014 vol.3, doi: https://doi.org/10.1109/MILCOM.1996.571434.
Chang, D., & Shih, P. (2015). Cumulants-based modulation classification technique in multipath fading channels. IET Communications, 9(6), 828–835. https://doi.org/10.1049/iet-com.2014.0773
Dobre, O. A., Oner, M., Rajan, S., & Inkol, R. (2012). Cyclostationarity-Based Robust Algorithms for QAM Signal Identification. IEEE Communications Letters, 16(1), 12–15. https://doi.org/10.1109/LCOMM.2011.112311.112006
Huang, Y., Jin, W., Li, B., Ge, P., & Wu, Y. (2019). Automatic modulation recognition of radar signals based on manhattan distance-based features. IEEE Access, 7, 41193–41204. https://doi.org/10.1109/ACCESS.2019.2907159
Wang, Y., Liu, M., Yang, J., & Gui, G. (2019). Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Transactions on Vehicular Technology, 68(4), 4074–4077. https://doi.org/10.1109/TVT.2019.2900460
Zhang, K., Xu, L., Feng, Z., & Zhang, P. (2019). A novel automatic modulation classification method based on dictionary learning. China Communications, 16(1), 176–192.
Ma, J., & Qiu, T. (2019). Automatic modulation classification using cyclic correntropy spectrum in impulsive noise. IEEE Wireless Communications Letters, 8(2), 440–443. https://doi.org/10.1109/LWC.2018.2875001
Meng, F., Chen, P., Wu, L., & Wang, X. (2018). Automatic modulation classification: A deep learning enabled approach. IEEE Transactions on Vehicular Technology, 67(11), 10760–10772. https://doi.org/10.1109/TVT.2018.2868698
Tsakmalis, A., Chatzinotas, S. and Ottersten, B. (2014). Modulation and coding classification for adaptive power control in 5G cognitive communications. In 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Toronto, ON, 2014, pp. 234-238, doi: https://doi.org/10.1109/SPAWC.2014.6941505.
Ding, H., Zhao, F., Tian, J., Li, D., & Zhang, H. (2020). A deep reinforcement learning for user association and power control in heterogeneous networks. Ad Hoc Networks, 102, 102069.
Mismar, F. B., Evans, B. L., & Alkhateeb, A. (2020). Deep reinforcement learning for 5G networks: Joint beamforming, power control, and interference coordination. IEEE Transactions on Communications, 68(3), 1581–1592. https://doi.org/10.1109/TCOMM.2019.2961332
Han, S., et al. (2019). Energy-efficient short packet communications for uplink NOMA-based massive MTC networks. IEEE Transactions on Vehicular Technology, 68(12), 12066–12078. https://doi.org/10.1109/TVT.2019.2948761
Algedir, A. A., & Refai, H. H. (2020). Energy efficiency optimization and dynamic mode selection algorithms for D2D communication under HetNet in downlink reuse. IEEE Access, 8, 95251–95265. https://doi.org/10.1109/ACCESS.2020.2995833
Olwal, T. O., Djouani, K. and Kurien, A. M. (2016). A survey of resource management toward 5G radio access networks. In IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 1656–1686, thirdquarter 2016, doi: https://doi.org/10.1109/COMST.2016.2550765.
Ali, A., Shah, G. A., & Arshad, J. (2019). Energy efficient resource allocation for M2M devices in 5G. Sensors, 19(8), 1830.
Usama, M., & Erol-Kantarci, M. (2019). A survey on recent trends and open issues in energy efficiency of 5G. Sensors, 19(14), 3126.
Shi, J., Wang, X. and Sun, L. (2015). Gray-model based SINR estimation for enhanced intercell interference coordination. In 2015 IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, 2015, pp. 631-635, doi: https://doi.org/10.1109/WCNC.2015.7127543.
Ikuno, J. C., Pendl, S., Šimko, M. and Rupp, M. (2012). Accurate SINR estimation model for system level simulation of LTE networks. In 2012 IEEE International Conference on Communications (ICC), Ottawa, ON, 2012, pp. 1471–1475, doi: https://doi.org/10.1109/ICC.2012.6364098.
Hong, S., Li, Y., He, Y., Wang, G., & Jin, M. (2012). A cyclic correlation-based blind SINR estimation for OFDM systems. IEEE Communications Letters, 16(11), 1832–1835. https://doi.org/10.1109/LCOMM.2012.100812.122009
Erpek, T., Sagduyu, Y. E., & Shi, Y. (2019). Deep learning for launching and mitigating wireless jamming attacks. IEEE Transactions on Cognitive Communications and Networking, 5(1), 2–14. https://doi.org/10.1109/TCCN.2018.2884910
Yun, S. and Caramanis, C. (2010). Reinforcement learning for link adaptation in MIMO-OFDM wireless systems. In 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, Miami, FL, 2010, pp. 1-5, doi: https://doi.org/10.1109/GLOCOM.2010.5683371.
Bennis, M. and Niyato, D. (2010). A Q-learning based approach to interference avoidance in self-organized femtocell networks. In 2010 IEEE Globecom Workshops, Miami, FL, 2010, pp. 706-710, doi: https://doi.org/10.1109/GLOCOMW.2010.5700414.
Mismar, F. B. and Evans, B. L. (2018). Q-learning algorithm for VoLTE closed loop power control in indoor small cells. In 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2018, pp. 1485–1489, doi: https://doi.org/10.1109/ACSSC.2018.8645168.
Choi, J. (2014). Massive MIMO with joint power control. IEEE Wireless Communications Letters, 3(4), 329–332. https://doi.org/10.1109/LWC.2014.2315039
Luo, C., Ji, J., Wang, Q., Yu, L. and Li, P. (2018). Online power control for 5G wireless communications: A deep q-network approach. In 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, 2018, pp. 1–6, doi: https://doi.org/10.1109/ICC.2018.8422442.
Wang, S., Liu, H., Gomes, P. H., & Krishnamachari, B. (2018). Deep reinforcement learning for dynamic multichannel access in wireless networks. IEEE Transactions on Cognitive Communications and Networking, 4(2), 257–265.
Alvarado, A. S., Lakshminarayan, C., & Principe, J. C. (2012). Time-based compression and classification of heartbeats. IEEE Transactions on Biomedical Engineering, 59(6), 1641–1648. https://doi.org/10.1109/TBME.2012.2191407
Kiranyaz, S., Ince, T., & Gabbouj, M. (2016). Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Transactions on Biomedical Engineering, 63(3), 664–675. https://doi.org/10.1109/TBME.2015.2468589
Jiang, W., & Kong, S. G. (2007). Block-based neural networks for personalized ECG signal classification. IEEE Transactions on Neural Networks, 18(6), 1750–1761. https://doi.org/10.1109/TNN.2007.900239
Llamedo, M., & Martínez, J. P. (2011). heartbeat classification using feature selection driven by database generalization criteria. IEEE Transactions on Biomedical Engineering, 58(3), 616–625. https://doi.org/10.1109/TBME.2010.2068048
Alonso-Atienza, F., Morgado, E., Fernández-Martínez, L., García-Alberola, A., & Rojo-Álvarez, J. L. (2014). Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Transactions on Biomedical Engineering, 61(3), 832–840. https://doi.org/10.1109/TBME.2013.2290800
Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Transactions on Biomedical Engineering, 61(6), 1607–1613. https://doi.org/10.1109/TBME.2013.2275000
Norolahi, J., Azmi, P., & Nasirian, M. (2022). Feasibility of a novel beamforming algorithm via retrieving spatial harmonics. Journal of Systems Engineering and Electronics, 33(1), 38–46. https://doi.org/10.23919/JSEE.2022.000005
Lee, K. H., & Verma, N. (2013). A low-power processor with configurable embedded machine-learning accelerators for high-order and adaptive analysis of medical-sensor signals. IEEE Journal of Solid-State Circuits, 48(7), 1625–1637. https://doi.org/10.1109/JSSC.2013.2253226
Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM transactions on intelligent systems and echnology (TIST), 2(3), 27.
Sun, Y., Ng, D. W. K., Ding, Z. and Schober, R. (2016). Optimal joint power and subcarrier allocation for MC-NOMA systems. In 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, 2016, pp. 1-6, doi: https://doi.org/10.1109/GLOCOM.2016.7842087.
Bishop, C. M. (2006). Pattern recognition and machine learning, New York, NY. Springer-Verlag.
Zarrinkoub, H. (2014). Understanding LTE with MATLAB: From mathematical foundation to simulation, performance evaluation and implementation. John Wiley & Sons Inc.
Zeng, M., Yadav, A., Dobre, O. A., Tsiropoulos, G. I., & Poor, H. V. (2017). Capacity comparison between MIMO-NOMA and MIMO-OMA with multiple users in a cluster. IEEE Journal on Selected Areas in Communications, 35(10), 2413–2424. https://doi.org/10.1109/JSAC.2017.2725879
Dobre, O. A., Abdi, A., Bar-Ness, Y., & Su, W. (2007). Survey of automatic modulation classification techniques: Classical approaches and new trends. IET Communications, 1(2), 137–156. https://doi.org/10.1049/iet-com:20050176
Wang, F., Huang, S., Wang, H., & Yang, C. (2018). Automatic modulation classification exploiting hybrid machine learning network. Mathematical Problems in Engineering, 2018, 1–14.
Güner, A., Alçin, Ö. F., & Şengür, A. (2019). Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features. Measurement, 145, 214–225.
Huang, S., Jiang, Y., Qin, X., Gao, Y., Feng, Z., & Zhang, P. (2018). Automatic modulation classification of overlapped sources using multi-gene genetic programming with structural risk minimization principle. IEEE Access, 6, 48827–48839.
Ghasemzadeh, P., Banerjee, S., Hempel, M. and Sharif, H. (2018). Performance evaluation of feature-based automatic modulation classification. In 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS), Cairns, Australia, 2018, pp. 1–5.
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Jafar Norolahi and Paeiz Azmi wrote the manuscript and reviwed it.
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Norolahi, J., Azmi, P. A machine learning based algorithm for joint improvement of power control, link adaptation, and capacity in beyond 5G communication systems. Telecommun Syst 83, 323–337 (2023). https://doi.org/10.1007/s11235-023-01017-1
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DOI: https://doi.org/10.1007/s11235-023-01017-1