Designing Smart Antennas Using Machine Learning Algorithms

• Autorzy: Samantaray Barsa , Das Kunal Kumar , Roy Jibendu Sekhar

Identyfikatory

DOI

10.26636/jtit.2023.4.1329

• Języki publikacji: EN

Abstrakty

EN

Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.

Słowa kluczowe

EN

artificial neural network; decision tree; ensemble algorithm; machine learning; smart antenna; support vector machine

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2023
• Tom: nr 4
• Strony: 46--52
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Samantaray Barsa

• ECE Department, ITER SOA University, Bhubaneswar, India

• https://orcid.org/0000-0001-7184-2123

autor

Das Kunal Kumar

• ECE Department, ITER SOA University, Bhubaneswar, India

autor

Roy Jibendu Sekhar

• School of Electronics Engineering KIIT University, Bhubaneswar, Odisha, India

• https://orcid.org/0000-0002-3571-2708

Bibliografia

• [1] C.A. Balanis, S. Bellofiore, J. Foutz, and A.S. Spanias, “Smart Antenna System for Mobile Communication Network”, IEEE Antenna and Propagation Magazine, vol. 44, no. 4, pp. 106 –114, 2002 (https://doi.org/10.1109/MAP.2002.1043158).
• [2] T.K. Sarkar, M.C. Wicks, M. Salazar-Palma, and R.J. Bonneu, Smart Antennas, Wiley IEEE Press, 2003 (ISBN: 0471210102).
• [3] C.A. Balanis, Antenna Theory: Analysis and Design, 3rd ed., Wiley, Hoboken, USA, 2005 (ISBN: 9780471667827).
• [4] M. Abualhayja’a and M. Houssain, “Comparative Study of Adaptive Beamforming Algorithms for Smart Antenna Applications”, International Conference on Communications, Signal Processing, and their Applications (ICCSPA), Sharjah, UAE, 2021 (https: //doi.org/10.1109/ICCSPA49915.2021.9385725).
• [5] M. Atzemourt, A. Farchi, Y. Chihab, and Z. Hachkar, ”Performance Evaluation of LMS and CM Algorithms for Beamforming”, Advances in Materials Science and Engineering, vol. 2022 , art. no. 7744625, 2022 (https://doi.org/10.1155/2022/7744625).
• [6] B. Samantaray, K.K. Das, and J.S. Roy, “Performance of Smart Antenna in Cellular Network Using Variable Step Size Algorithms”, International Journal of Microwave and Optical Technology (IJMOT), vol. 15 , no. 2, pp. 179 –186, 2020 (https://www.ijmot.com/VOL- 15-NO-2.aspx).
• [7] K. Pirapaharan, N. Ajithkumar, K. Sarujan, X. Fernando, and P.R.P. Hoole, “Smart, Fast, and Low Memory Beam-steering Antenna Configurations for 5G and Future Wireless Systems”, Electronics, vol. 11, no. 17, art no. 2658, 2022 (https://doi.org/ 10. 3390/electronics11172658).
• [8] B.C. Banister and J.R. Zeidler, “Tracking Performance of the RLS Algorithm Applied to an Antenna Array in a Realistic Fading Environment”, IEEE Transactions on Signal Processing, vol. 50, no. 5, pp. 1037–1050 , 2002 (https://doi.org/ 10.1109/78.995061).
• [9] J. Gao, J. Zhen, Y. Lv, and B. Guo, “Beamforming Technique Based on Adaptive Diagonal Loading in Wireless Access Networks”, Ad Hoc Networks, vol. 107 , art. no. 102249 , 2020 (https://doi.org/10.1016/j.adhoc.2020.102249).
• [10] B. Boustani, A. Baghdad, A. Sahel, A. Badri, and A. Ballouk, “Adaptive Algorithms for Smart Antenna System”, 2018 6 th International Conference on Multimedia Computing and Systems (ICMCS), Rabat, Morocco, 2018 (https://doi.org/ 10.1109/ICMCS.2018. 8525905).
• [11] Veerendra, M. Bakhar, and R.M. Vani, “Robust Blind Beam Formers for Smart Antenna System Using Window Techniques”, Procedia Computer Science, vol. 93, pp. 713– 720, 2016 (https://doi.org/10.1016/j.procs.2016.07.274).
• [12] S. Khamayseh and A. Halawani, “Cooperative Spectrum Sensing In Cognitive Radio Networks: A Survey on Machine Learning-based Methods”, Journal of Telecommunication and Information Technology, vol. 2020 , no. 3, pp. 36– 46, 2020 (https://doi.org/ 10.26636/jtit.2020.137219).
• [13] F.Z. Fagroud et al., “Connected Devices Classification Using Feature Selection with Machine Learning”, IAENG International Journal of Computer Science, vol. 49, no. 2, pp. 445 –452, 2022 (https://www.iaeng.org/IJCS/issues_v49/issue_2/IJCS_49_2_18.pdf).
• [14] A. Rawat, R.N. Yadav, and S.C. Shrivastava. “Neural Network Applications in Smart Antenna Arrays: A Review”, AEU-International Journal of Electronics and Communications, vol. 66, no. 11, pp. 903–912, 2012 (https://doi.org/ 10.1016/j.aeue.2012.03.012).
• [15] B. Hamdi, S. Limam, and T. Aguili, “Uniform and Concentric Circular Antenna Arrays Synthesis for Smart Antenna Systems Using Artificial Neural Network Algorithm”, Progress In Electromagnetics Research B, vol. 67, pp. 91– 105, 2016 (https://doi.org/ 10.2528/PIERB16031508).
• [16] F. Andriulli, P.-Y. Chen, D. Erricolo, and J.-M. Jin, “Guest Editorial. Machine Learning in Antenna Design, Modeling, and Measurements”, IEEE Transactions on Antennas and Propagation, vol. 70 , no. 7, pp. 4948–4952 , 2022 (https://doi.org/ 10.1109/TAP.2022.3189963).
• [17] D. Shi, C. Lian, K. Cui, Y. Chen and X. Liu, “An Intelligent Antenna Synthesis Method Based on Machine Learning”, IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 4965– 4976, 2022 (https://doi.org/10.1109/TAP.2022.3182693).
• [18] A. Ibrahim, H.F. Abutarboush, A. Wagdy, M.M. Fouad, and E-S.M. Elkenawy, “An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna”, Computers, Materials & Continua, vol. 71, no. 1, pp. 199– 213, 2022 (https://doi.org/ 10.32604/cmc.2022.021886).
• [19] Y. Anzai, Pattern Recognition and Machine Learning, Elsevier, 2012 (ISBN: 9780080513638).
• [20] I.H. Sarker, “Machine Learning: Algorithms, Real -world Applications and Research Directions”, SN Computer Science, vol. 2, no. 3, pp. 1–21, 2021 (https://doi.org/10.1007/s42979-021-00592-x).
• [21] S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, “Improvements to Platt’s SMO Algorithm for SVM Classifier Design”, Neural Computation, vol. 13, no. 3, pp. 637– 649, 2001 (https: //doi.org/10.1162/089976601300014493).
• [22] J. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Microsoft Technical Report MSR-TR- 98- 14, 1998 (https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.doc).
• [23] S. Haykin, Neural Network and Machine Learning, 3rd ed., Pearson International, 936 p., 2009 (ISBN: 9780131471399).
• [24] Y. Han, Y. Tao, W. Zhang, W. Cui, and T. Shi, “Perceptron Neural Network Image Encryption Algorithm Based on Chaotic System”, IAENG International Journal of Computer Science, vol. 50, no.1, pp. 42–50, 2023 (https://www.iaeng.org/IJCS/issues_v50/issue_1/IJCS_50_1_06.pdf).
• [25] Y. Ye, J. Che, and H. Wang, “Optimal Component IGSCV-SVR Ensemble Model Improved by VMD for Ultra-short-term Wind Speed Forecasting”, Engineering Letters, IAENG, vol. 30, no. 3, pp. 1166–1175, 2022 (https://www.engineeringletters.com/issues_v30/issue_3/EL_30_3_29.pdf).
• [26] Y. Yang, Temporal Data Mining via Unsupervised Ensemble Learning, Elsevier, USA, 172 p., 2016 (ISBN: 9780128116548).
• [27] M.S. Alajmi and A.M. Almeshal, “Least Squares Boosting Ensemble and Quantum-behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material”, Applied Sciences, vol. 11 , no. 5, art. no. 2126 , 2021 (https://doi.org/10.3390/app11052126).
• [28] J.R. Quinlan, “Induction of Decision Trees”, Machine Learning, vol. 1, no. 1, pp. 81–106 , 1986 (https://doi.org/ 10.1007/BF00116251).
• [29] B.T. Jijo and A.M. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning”, Journal of Applied Science and Technology Trends, vol. 2, no. 1, pp. 20–28, 2021 (https://doi.org/10.38094/jastt20165).
• [30] Y. Yang, I.G. Morillo, and T.M. Hospedales, “Deep Neural Decision Trees”, 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden, 2018 (https://doi.org/10.48550/arXiv.1806.06988).
• [31] Y.-S. Lau, Z.M. Hussain, and R.J. Harris, “A Weight-vector LMS Algorithm for Adaptive Beamforming”, IEEE Region 10 ConferenceTENCON, Chiang Mai, Thailand, pp. 494– 498, 2004 (https://doi.org/10.1109/TENCON.2004.1414465).
• [32] H. Che, C. Li, X. He, and T. Huang, “A Recurrent Neural Network for Adaptive Beamforming and Array Correction”, Neural Networks, vol. 80, pp. 110–117, 2016 (https://doi.org/ 10.1016/j.neunet.2016.04.010).
• [33] I. Mallioras, Z.D. Zaharis, P.I. Lazaridis, and S. Pantelopoulos, “A novel realistic approach of adaptive beamforming based on deep neural networks”, IEEE Transactions on Antennas and Propagation, vol. 70, no. 10, pp. 8833– 8848, 2022 (https://doi.org/ 10. 1109/TAP.2022.3168708).
• [34] A.H. Sallomi and S. Ahmed, “Elman Recurrent Neural Network Application in Adaptive Beamforming of Smart Antenna System”, International Journal of Computer Applications (IJCA), vol. 129, no. 11, pp. 38–43, 2015 (https://doi.org/ 10.5120/ijca2015907041).
• [35] F. Liu, Y. Wu, H. Duan, and R. Du, “SVR-CMT Algorithm for Null Broadening and Sidelobe Control”, Progress In Electromagnetics Research, vol. 163 , pp. 39–50, 2018 (https://doi.org/ 10. 2528/PIER18061106).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).