Juan Diego Belesaca
ABSTRACT
Next-generation wireless technologies face considerable challenges in terms of providing the required latency and connectivity for new heterogeneous mobile networks. Driven by these problems, this study focuses on increasing user connectivity together with system throughput. For doing so, we propose and evaluate a hybrid machine learning-driven orthogonal/non-orthogonal multiple access (OMA/NOMA) system. In this work, we use an artificial neural network (ANN) to assign an OMA or NOMA access method to each user equipment (UE). As part of this research we also evaluate the accuracy and training time of the three most relevant learning algorithms of ANN (L-M, BFGS, and OSS). The main objective is to increase the sum-rate of the mobile network in the introduced beamforming and mmWave channel environment.
Simulation results show up to a $20%$ sum-rate average performance increase of the system using the ANN management in contrast to a random non-ANN managed system. The Leveberg-Marquard (L-M) training algorithm is the best overall algorithm for this proposed application as presents the highest accuracy of around $77%$ despite 37 minutes of training and lower accuracy of $73%$ with approximately 28 seconds of training time.
- Mahmoud Aldababsa, Mesut Toka, Selahattin Gö kcc eli, Gü neG Karabulut Kurt, and OLuz Kucur. 2018. A Tutorial on Nonorthogonal Multiple Access for 5G and Beyond. (2018). https://doi.org/10.1155/2018/9713450Google Scholar
- M. Alkhawatrah, Y. Gong, G. Chen, S. Lambotharan, and J. A. Chambers. 2019. Buffer-Aided Relay Selection for Cooperative NOMA in the Internet of Things. IEEE Internet of Things Journal, Vol. 6, 3 (2019), 5722--5731. https://doi.org/10.1109/JIOT.2019.2905169Google ScholarCross Ref
- Roberto Battiti. 1992. First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method. Neural Computation, Vol. 4, 2 (mar 1992), 141--166. https://doi.org/10.1162/neco.1992.4.2.141 Google ScholarDigital Library
- Yunlong Cai, Zhijin Qin, Fangyu Cui, Geoffrey Ye Li, and Julie A. McCann. 2018. Modulation and Multiple Access for 5G Networks. IEEE Communications Surveys and Tutorials, Vol. 20, 1 (2018), 629--646. https://doi.org/10.1109/COMST.2017.2766698 arxiv: 1702.07673Google ScholarCross Ref
- Pablo Avila Campos. 2019. Beamformer Design and Performance in mmWave-NOMA Systems. Master's thesis. University of Southampton.Google Scholar
- Zafer CÖ MERT and Adnan KOCAMAZ. 2017. A Study of Artificial Neural Network Training Algorithms for Classification of Cardiotocography Signals. Bitlis Eren University Journal of Science and Technology, Vol. 7, 2 (2017), 93--103. https://doi.org/10.17678/beuscitech.338085Google ScholarCross Ref
- Jingjing Cui, Zhiguo Ding, and Pingzhi Fan. 2018a. The Application of Machine Learning in mmWave-NOMA Systems. IEEE Vehicular Technology Conference, Vol. 2018-June (2018), 1--6. https://doi.org/10.1109/VTCSpring.2018.8417523Google Scholar
- Jingjing Cui, Zhiguo Ding, Pingzhi Fan, and Naofal Al-Dhahir. 2018b. Unsupervised machine learning-based user clustering in millimeter-Wave-NOMA systems. IEEE Transactions on Wireless Communications, Vol. 17, 11 (2018), 7425--7440. https://doi.org/10.1109/TWC.2018.2867180Google ScholarCross Ref
- Linglong Dai, Bichai Wang, Yifei Yuan, Shuangfeng Han, Chih Lin I, and Zhaocheng Wang. 2015. Non-orthogonal multiple access for 5G: Solutions, challenges, opportunities, and future research trends. IEEE Communications Magazine, Vol. 53, 9 (2015), 74--81. https://doi.org/10.1109/MCOM.2015.7263349Google ScholarDigital Library
- F. Dario Baptista, Sandy Rodrigues, and Fernando Morgado-Dias. 2013. Performance comparison of ANN training algorithms for classification. 2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings (2013), 115--120. https://doi.org/10.1109/WISP.2013.6657493Google ScholarCross Ref
- J. E. Dennis and Robert B. Schnabel. 1996. Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611971200 Google ScholarDigital Library
- Zhiguo Ding, Xianfu Lei, George K. Karagiannidis, Robert Schober, Jinhong Yuan, and Vijay K. Bhargava. 2017a. A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends. IEEE Journal on Selected Areas in Communications, Vol. 35, 10 (2017), 2181--2195. https://doi.org/10.1109/JSAC.2017.2725519 arxiv: 1706.05347Google ScholarDigital Library
- Zhiguo Ding, Yuanwei Liu, Jinho Choi, Qi Sun, Maged Elkashlan, Chih-Lin I, and H. Vincent Poor. 2017b. Application of Non-Orthogonal Multiple Access in LTE and 5G Networks. IEEE Communications Magazine, Vol. 55, 2 (feb 2017), 185--191. https://doi.org/10.1109/MCOM.2017.1500657CM Google ScholarDigital Library
- Zhiguo Ding, Senior Member, Xianfu Lei, George K Karagiannidis, Robert Schober, Jinhong Yuan, Vijay K Bhargava, and Life Fellow. 2017c. A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol. 35, 10 (2017). https://doi.org/10.1109/JSAC.2017.2725519Google ScholarCross Ref
- M. M. El-Sayed, A. S. Ibrahim, and M. M. Khairy. 2016. Power allocation strategies for Non-Orthogonal Multiple Access. In 2016 International Conference on Selected Topics in Mobile Wireless Networking (MoWNeT). 1--6.Google Scholar
- Ericsson. 2019. Ericsson Mobility Report June 2019. Technical Report. www.ericsson.com/mobility-reportGoogle Scholar
- Longinus S. Ezema and Cosmas I. Ani. 2017. Artificial Neural Network Approach to Mobile Location Estimation in GSM Network. International Journal of Electronics and Telecommunications, Vol. 63, 1 (2017), 39--44.Google ScholarCross Ref
- Daniel Graupe. 2013. Principles of Artificial Neural Networks. Advanced Series in Circuits and Systems, Vol. 6. WORLD SCIENTIFIC. 1--281 pages. https://doi.org/10.1017/CBO9781107415324.004 arxiv: arXiv:1011.1669v3 Google ScholarDigital Library
- Guan Gui, Hongji Huang, Yiwei Song, and Hikmet Sari. 2018. Deep Learning for an Effective Nonorthogonal Multiple Access Scheme. IEEE Transactions on Vehicular Technology, Vol. 67, 9 (2018), 8440--8450. https://doi.org/10.1109/TVT.2018.2848294Google ScholarCross Ref
- Martin T. Hagan and Mohammad B. Menhaj. 1994. Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks, Vol. 5, 6 (1994), 989--993. https://doi.org/10.1109/72.329697 Google ScholarDigital Library
- Hamidreza Hassannejad, Mohammad Sirus, and Rasool Mehdizadeh. 2015. Comparison and Evaluation of Artificial Neural Network ( ANN ) Training Algorithms in Predicting Soil Type Classification. Bulletin of Environment, Pharmacology and Life Sciences, Vol. 4, 1 (2015), 212--218. http://www.bepls.comGoogle Scholar
- Kenichi Higuchi and Anass Benjebbour. 2015. Non-Orthogonal Multiple Access (NOMA) with successive interference cancellation for future radio access. IEICE Transactions on Communications, Vol. E98B, 3 (mar 2015), 403--414. https://doi.org/10.1587/transcom.E98.B.403Google Scholar
- Mark Hudson, Beale Martin, T Hagan, and Howard B Demuth. 1992. Neural Network Toolbox? 7 User's Guide. Technical Report. www.mathworks.comGoogle Scholar
- S. M.Riazul Islam, Nurilla Avazov, Octavia A. Dobre, and Kyung Sup Kwak. 2017. Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges. IEEE Communications Surveys and Tutorials, Vol. 19, 2 (2017), 721--742. https://doi.org/10.1109/COMST.2016.2621116Google ScholarDigital Library
- Komal Janghel and Shankar Prakriya. 2018. Performance of Adaptive OMA/Cooperative-NOMA Scheme With User Selection. IEEE Communications Letters, Vol. 22, 10 (2018), 2092--2095. https://doi.org/10.1109/LCOMM.2018.2863365Google ScholarCross Ref
- Hesam Karim, Sharareh R. Niakan, and Reza Safdari. 2018. Comparison of neural network training algorithms for classification of heart diseases. IAES International Journal of Artificial Intelligence, Vol. 7, 4 (2018), 185--189. https://doi.org/10.11591/ijai.v7.i4.pp185--189Google ScholarCross Ref
- N. Kato, Z. M. Fadlullah, B. Mao, F. Tang, O. Akashi, T. Inoue, and K. Mizutani. 2017. The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective. IEEE Wireless Communications, Vol. 24, 3 (2017), 146--153. https://doi.org/10.1109/MWC.2016.1600317WCGoogle ScholarDigital Library
- Karabulut Kurt, Muhammad Zeeshan Shakir, and Imran Shafique Ansari. 2018. Nonorthogonal Multiple Access for 5G and beyond. Wireless Communications and Mobile Computing, Vol. 2018, 12 (2018), 2347--2381. https://doi.org/10.1155/2018/1907506Google Scholar
- Wei Liu and Stephan Weiss. 2010. Adaptive Wideband Beamforming. In Wideband Beamforming. John Wiley & Sons, Ltd, Chichester, UK, 19--60. https://doi.org/10.1002/9780470661178.ch2Google Scholar
- Tiejun Lv, Yuyu Ma, Jie Zeng, and P. Takis Mathiopoulos. 2018. Millimeter-Wave NOMA Transmission in Cellular M2M Communications for Internet of Things. IEEE Internet of Things Journal, Vol. 5, 3 (jun 2018), 1989--2000. https://doi.org/10.1109/JIOT.2018.2819645Google ScholarCross Ref
- Qian Mao, Fei Hu, and Qi Hao. 2018. Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, Vol. 20, 4 (2018), 2595--2621. https://doi.org/10.1109/COMST.2018.2846401Google ScholarDigital Library
- Narengerile and John Thompson. 2019. Deep Learning for Signal Detection in Non-Orthogonal Multiple Access Wireless Systems. 2019 UK/China Emerging Technologies, UCET 2019 (2019), 1--4. https://doi.org/10.1109/UCET.2019.8881888Google Scholar
- Nikolaos Nomikos, Themistoklis Charalambous, Demosthenes Vouyioukas, George K. Karagiannidis, and Risto Wichman. 2019. Hybrid NOMA/OMA with Buffer-Aided Relay Selection in Cooperative Networks. IEEE Journal on Selected Topics in Signal Processing, Vol. 13, 3 (2019), 524--537. https://doi.org/10.1109/JSTSP.2019.2894059Google ScholarCross Ref
- H. Hasan Ö rkcü and Hasan Bal. 2011. Comparing performances of backpropagation and genetic algorithms in the data classification. Expert Systems with Applications, Vol. 38, 4 (2011), 3703--3709. https://doi.org/10.1016/j.eswa.2010.09.028 Google ScholarDigital Library
- Dan W. Patterson. 1996. Artificial Neural Networks: Theory and Applications ., 477 pages. Google ScholarDigital Library
- Juan Pereda, Javier De Lope, and Darío Maravall. 2006. Comparative analysis of artificial neural network training methods for inverse kinematics learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4177 LNAI (2006), 171--179. https://doi.org/10.1007/11881216_19 Google ScholarDigital Library
- Rohde& Schwarz. [n.d.]. Millimeter-Wave Beamforming: Antenna Array Design Choices & Characterization. https://www.rohde-schwarz.com/applications/millimeter-wave-beamforming-antenna-array-design-choices-characterization-white-paper_230854--325249.html?rusprivacypolicy=0Google Scholar
- Zhanji Wu, Kun Lu, Chengxin Jiang, and Xuanbo Shao. 2018. Comprehensive Study and Comparison on 5G NOMA Schemes. IEEE Access, Vol. 6 (2018), 18511--18519. https://doi.org/10.1109/ACCESS.2018.2817221Google ScholarCross Ref
- Peng Xu, Jianping Quan, Zheng Yang, Gaojie Chen, and Zhiguo Ding. 2019. Performance Analysis of Buffer-Aided Hybrid NOMA/OMA in Cooperative Uplink System. IEEE Access, Vol. 7 (2019), 168759--168773. https://doi.org/10.1109/ACCESS.2019.2955118Google ScholarCross Ref
- Lipeng Zhu, Jun Zhang, Zhenyu Xiao, Xianbin Cao, Dapeng Oliver Wu, and Xiang Gen Xia. 2018. Joint Power Control and Beamforming for Uplink Non-Orthogonal Multiple Access in 5G Millimeter-Wave Communications. IEEE Transactions on Wireless Communications, Vol. 17, 9 (sep 2018), 6177--6189. https://doi.org/10.1109/TWC.2018.2855151 arxiv: 1711.06162Google ScholarCross Ref
Index Terms
- Artificial Neural Network Performance Evaluation for a Hybrid Power Domain Orthogonal / Non-Orthogonal Multiple Access (OMA / NOMA) System
Recommendations
Non orthogonal multiple access with solar energy harvesting
AbstractIn this paper, we derive the throughput of wireless communications when the source harvests energy using a solar panel. We compute the statistics of the Signal to Noise Ratio (SNR) and that of the Signal to Interference plus Noise ...
Orthogonal multiple access over time- and frequency-selective channels
Suppression of multiuser interference (MUI) and mitigation of time- and frequency-selective (doubly selective) channel effects constitute major challenges in the design of third-generation wireless mobile systems. Relying on a basis expansion model (BEM)...
Performance analysis of non-orthogonal multiple access in 5G mm-wave wireless networks
Wireless systems in the fifth generation (5G) are expected to satisfy high capacity and latency demands. Non-Orthogonal Multiple Access (NOMA) is a significant emerging paradigm for 5G to fulfil diverse needs for low latency, enhanced fairness, improved ...
Comments