Comparative Study of 5G Signal Attenuation Estimation Models
Subject Areas : Communication Systems & Devices
Md Anoarul
Islam
1
(Department of Electronics and Communication Engineering, Techno International New Town)
Manabendra
Maiti
2
(Department of Electronics and Communication Engineering, Techno International New Town)
Judhajit
Sanyal
3
(Department of Electronics and Communication Engineering, Techno International New Town)
Quazi Md
Alfred
4
(Department of Electronics and Communication Engineering, Aliah University)
Keywords: 5G, Estimation, Attenuation Models, Machine Learning, Dynamic Model, Autoregression,
Abstract :
Wireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent years has consequently focused on establishment of robust and accurate attenuation models to estimate channel noise and subsequent signal loss. The identified challenge therefore is to identify or develop accurate computationally inexpensive models implementable on available hardware for generation of estimates with low error and validate the solutions experimentally. The present work surveys some of the most relevant recent work in this domain, with added emphasis on rain attenuation models and machine learning based approaches, and offers a perspective on the establishment of a suitable dynamic signal attenuation model for high-speed wireless communication in outdoor as well as indoor environments, presenting the performance evaluation of an autoregression-based machine learning model. Multiple versions of the model are compared on the basis of root mean square error (RMSE) for different orders of regression polynomials to find the best-fit solution. The accuracy of the technique proposed in the paper is then compared in terms of RMSE to corresponding moderate and high complexity machine learning techniques implementing adaptive spline regression and artificial neural networks respectively. The proposed method is found to be quite accurate with low complexity, allowing the method to be practically applicable in multiple scenarios.
[1] T. S. Rappaport et al., "Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!," in IEEE Access, vol. 1, pp. 335-349, 2013, doi: 10.1109/ACCESS.2013.2260813.
[2] T. S. Rappaport, Y. Xing, G. R. MacCartney, A. F. Molisch, E. Mellios and J. Zhang, "Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models," in IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp. 6213-6230, Dec. 2017, doi: 10.1109/TAP.2017.2734243.
[3] H. H. Hussein, H. A. Elsayed & S. M. Abd El-kader, “Intensive Benchmarking of D2D communication over 5G cellular networks: prototype, integrated features, challenges, and main applications,” Wireless Networks, vol. 26, pp. 3183–3202, 2020. https://doi.org/10.1007/s11276-019-02131-2.
[4] Y. Sun, J. Cao, M. Ma, Y. Zhang, H. Li and B. NIU, "EAP-DDBA: Efficient Anonymity Proximity Device Discovery and Batch Authentication Mechanism for Massive D2D Communication Devices in 3GPP 5G HetNet," in IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/TDSC.2020.2989784.
[5] S. N. Livieratos and P. G. Cottis, "Rain Attenuation Along Terrestrial Millimeter Wave Links: A New Prediction Method Based on Supervised Machine Learning," in IEEE Access, vol. 7, pp. 138745-138756, 2019, doi: 10.1109/ACCESS.2019.2939498.
[6] J. Huang, Y. Cao, X. Raimundo, A. Cheema and S. Salous, "Rain Statistics Investigation and Rain Attenuation Modeling for Millimeter Wave Short-Range Fixed Links," in IEEE Access, vol. 7, pp. 156110-156120, 2019, doi: 10.1109/ACCESS.2019.2949437.
[7] A. M. Al-Saman, M. Cheffena, M. Mohamed, M. H. Azmi and Y. Ai, "Statistical Analysis of Rain at Millimeter Waves in Tropical Area," in IEEE Access, vol. 8, pp. 51044-51061, 2020, doi: 10.1109/ACCESS.2020.2979683.
[8] Z. A. Shamsan, "Rainfall and Diffraction Modeling for Millimeter-Wave Wireless Fixed Systems," in IEEE Access, vol. 8, pp. 212961-212978, 2020, doi: 10.1109/ACCESS.2020.3040624.
[9] F. D. Diba, M. A. Samad and D. -Y. Choi, "The Effects of Rain on Terrestrial Links at K, Ka and E-Bands in South Korea: Based on Supervised Learning," in IEEE Access, vol. 9, pp. 9345-9355, 2021, doi: 10.1109/ACCESS.2021.3049825.
[10] A. A. H. Budalal, M. R. Islam, K. Abdullah and T. Abdul Rahman, "Modification of Distance Factor in Rain Attenuation Prediction for Short-Range Millimeter-Wave Links," in IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 6, pp. 1027-1031, June 2020, doi: 10.1109/LAWP.2020.2987462.
[11] Z. A. Shamsan, "Dust Storm and Diffraction Modelling for 5G Spectrum Wireless Fixed Links in Arid Regions," in IEEE Access, vol. 7, pp. 162828-162840, 2019, doi: 10.1109/ACCESS.2019.2951855.
[12] H. B. H. Dutty and M. M. Mowla, "Channel Modeling at Unlicensed Millimeter Wave V Band for 5G Backhaul Networks," 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), 2019, pp. 769-773, doi: 10.1109/ICAEE48663.2019.8975439.
[13] A. Hikmaturokhman, M. Suryanegara and K. Ramli, "A Comparative Analysis of 5G Channel Model with Varied Frequency: A Case Study in Jakarta," 2019 7th International Conference on Smart Computing & Communications (ICSCC), 2019, pp. 1-5, doi: 10.1109/ICSCC.2019.8843632.
[14] S. K. M., S. A. Rao and N. Kumar, "Modeling and Link Budget Estimation of Directional mmWave Outdoor Environment for 5G," 2019 European Conference on Networks and Communications (EuCNC), 2019, pp. 106-111, doi: 10.1109/EuCNC.2019.8802001.
[15] Y. Dalveren, A. H. Alabish and A. Kara, "A Simplified Model for Characterizing the Effects of Scattering Objects and Human Body Blocking Indoor Links at 28 GHz," in IEEE Access, vol. 7, pp. 69687-69691, 2019, doi: 10.1109/ACCESS.2019.2919546.
[16] X. Cai, G. Zhang, C. Zhang, W. Fan, J. Li and G. F. Pedersen, "Dynamic Channel Modeling for Indoor Millimeter-Wave Propagation Channels Based on Measurements," in IEEE Transactions on Communications, vol. 68, no. 9, pp. 5878-5891, Sept. 2020, doi: 10.1109/TCOMM.2020.3001614.
[17] M. K. Arti, "Channel Estimation and Detection in Satellite Communication Systems," in IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 10173-10179, Dec. 2016, doi: 10.1109/TVT.2016.2529661.
[18] C. Lu et al., "A New Rain Attenuation Prediction Model for the Earth-Space Links," in IEEE Transactions on Antennas and Propagation, vol. 66, no. 10, pp. 5432-5442, Oct. 2018, doi: 10.1109/TAP.2018.2854181.
[19] J. X. Yeo, Y. H. Lee and J. T. Ong, "Rain Attenuation Prediction Model for Satellite Communications in Tropical Regions," in IEEE Transactions on Antennas and Propagation, vol. 62, no. 11, pp. 5775-5781, Nov. 2014, doi: 10.1109/TAP.2014.2356208.
[20] S. Mohanty, C. Singh and V. Tiwari, "Estimation of rain attenuation losses in signal link for microwave frequencies using ITU-R model," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, pp. 532-535, doi: 10.1109/IGARSS.2016.7729132.
[21] I. Shayea, T. Abd. Rahman, M. Hadri Azmi and M. R. Islam, "Real Measurement Study for Rain Rate and Rain Attenuation Conducted Over 26 GHz Microwave 5G Link System in Malaysia," in IEEE Access, vol. 6, pp. 19044-19064, 2018, doi: 10.1109/ACCESS.2018.2810855.
[22] S. Sun, T. S. Rappaport, M. Shafi, P. Tang, J. Zhang and P. J. Smith, "Propagation Models and Performance Evaluation for 5G Millimeter-Wave Bands," in IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8422-8439, Sept. 2018, doi: 10.1109/TVT.2018.2848208.
[23] Md Anoarul Islam, Kausik Bhattacharya, Md. Abdul Alim Sheikh, P. K. Ghosh, Manabendra Maiti, Judhajit Sanyal, "Machine Learning Reinforced Ku-band Rain Attenuation Model for Tropical Locations", in 2019 Global Conference for Advancement in Technology (GCAT), Bangalore, India, October 18-20, 2019.
[24] M. A. Islam, M. Maiti, P. K. Ghosh & J. Sanyal, “Machine Learning-Based Rain Attenuation Prediction Model,” In: Das N.R., Sarkar S. (eds) Computers and Devices for Communication. CODEC 2019. Lecture Notes in Networks and Systems, vol 147. Springer, Singapore. https://doi.org/10.1007/978-981-15-8366-7_3.
[25] N. O. Oyie and T. J. O. Afullo, "Measurements and Analysis of Large-Scale Path Loss Model at 14 and 22 GHz in Indoor Corridor," in IEEE Access, vol. 6, pp. 17205-17214, 2018, doi: 10.1109/ACCESS.2018.2802038.
[26] M. A. Islam, M. Maiti, Q. M. Alfred, P. Kumar Ghosh and J. Sanyal, "Attenuation Modelling and Machine Learning Based SNR Estimation for 5G Indoor Link," 2020 IEEE VLSI DEVICE CIRCUIT AND SYSTEM (VLSI DCS), 2020, pp. 275-278, doi: 10.1109/VLSIDCS47293.2020.9179924.
[27] Noman Shabbir, Lauri Kütt, Muhammad M. Alam, Priit Roosipuu, Muhammad Jawad, Muhammad B. Qureshi, Ali R. Ansari, Raheel Nawaz, “Vision towards 5G: Comparison of radio propagation models for licensed and unlicensed indoor femtocell sensor networks”, Physical Communication, Volume 47, 2021. https://doi.org/10.1016/j.phycom.2021.101371.
[28] D. Makoveyenko, S. Siden and V. Pyliavskyi, "Generalized 5G mmWave Propagation Model in an Urban Macro Environment," 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T), 2020, pp. 472-476, doi: 10.1109/PICST51311.2020.9468030.
[29] M. Sung et al., "RoF-Based Radio Access Network for 5G Mobile Communication Systems in 28 GHz Millimeter-Wave," in Journal of Lightwave Technology, vol. 38, no. 2, pp. 409-420, 15 Jan.15, 2020, doi: 10.1109/JLT.2019.2942636.
[30] H. Kim and S. Nam, "Transmission Enhancement Methods for Low-Emissivity Glass at 5G mmWave Band," in IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 1, pp. 108-112, Jan. 2021, doi: 10.1109/LAWP.2020.3042524.
[31] J. Liu, D. W. Matolak, M. Mohsen and J. Chen, "Path Loss Modeling and Ray-Tracing Verification for 5/31/90 GHz Indoor Channels," 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019, pp. 1-6, doi: 10.1109/VTCFall.2019.8891181.
[32] H. A. Obeidat et al., "An indoor path loss prediction model using wall correction factors for wireless local area network and 5G indoor networks," in Radio Science, vol. 53, no. 4, pp. 544-564, April 2018, doi: 10.1002/2018RS006536.
[33] C. A. L. Diakhate, J. Conrat, J. Cousin and A. Sibille, "Millimeter-wave outdoor-to-indoor channel measurements at 3, 10, 17 and 60 GHz," 2017 11th European Conference on Antennas and Propagation (EUCAP), 2017, pp. 1798-1802, doi: 10.23919/EuCAP.2017.7928696.
[34] F. Yu et al., "Expansion RSS-based Indoor Localization Using 5G WiFi Signal," 2014 International Conference on Computational Intelligence and Communication Networks, 2014, pp. 510-514, doi: 10.1109/CICN.2014.117.
[35] H. Echigo, Y. Cao, M. Bouazizi and T. Ohtsuki, "A Deep Learning-Based Low Overhead Beam Selection in mmWave Communications," in IEEE Transactions on Vehicular Technology, vol. 70, no. 1, pp. 682-691, Jan. 2021, doi: 10.1109/TVT.2021.3049380.
[36] Gante, J., Falcão, G. & Sousa, L. Deep Learning Architectures for Accurate Millimeter Wave Positioning in 5G. Neural Process Lett 51, 487–514 (2020). https://doi.org/10.1007/s11063-019-10073-1.
[37] H. Echigo, T. Ohtsuki, W. Jiang and Y. Takatori, "Fair Pilot Assignment Based on AOA and Pathloss with Location Information in Massive MIMO," GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017, pp. 1-6, doi: 10.1109/GLOCOM.2017.8254813.
[38] H. Echigo and T. Ohtsuki, "Graph Coloring-Based Pilot Reuse with AOA and Distance in D2D Underlay Massive MIMO," 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), 2018, pp. 1-6, doi: 10.1109/VTCSpring.2018.8417577.
[39] S. Moon, H. Kim and I. Hwang, "Deep learning-based channel estimation and tracking for millimeter-wave vehicular communications," in Journal of Communications and Networks, vol. 22, no. 3, pp. 177-184, June 2020, doi: 10.1109/JCN.2020.000012.
[40] J. Pudashine et al., "Probabilistic Attenuation Nowcasting for the 5G Telecommunication Networks," in IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 6, pp. 973-977, June 2021, doi: 10.1109/LAWP.2021.3068393.
[41] M. J. Hasan, A. Rai, Z. Ahmad and J. -M. Kim, "A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning," in IEEE Access, vol. 9, pp. 58052-58066, 2021, doi: 10.1109/ACCESS.2021.3072854.
[42] Hasan, Md J., Dongkoo Shon, Kichang Im, Hyun-Kyun Choi, Dae-Seung Yoo, and Jong-Myon Kim. 2020. "Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals" Applied Sciences 10, no. 21: 7639. https://doi.org/10.3390/app10217639.
[43] M. J. Hasan, J. Uddin and S. N. Pinku, "A novel modified SFTA approach for feature extraction," 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2016, pp. 1-5, doi: 10.1109/CEEICT.2016.7873115.