SNR Prediction Based on Environmental Sensing Data: An Approach Using Machine Learning
Main Article Content
Abstract
Signal-to-Noise Ratio (SNR) is a critical metric for assessing wireless link quality and optimizing various aspects of wireless communication, such as modulation level, coding scheme, handover decisions, and antenna configuration. While prior research has primarily focused on SNR prediction based on channel state information using feedback channels, this approach has limitations in terms of applicability and efficiency. In this paper, we propose a novel machine learning-based approach for SNR prediction that leverages environmental sensing data, eliminating the need for feedback channels. Our methodology harnesses the untapped potential of environmental factors, such as soil and air characteristics, to enhance SNR prediction accuracy. By intelligently fusing these environmental parameters with machine learning algorithms, we develop an adaptable SNR prediction model that can effectively capture the dynamics of wireless environments. Experimental results demonstrate the potential to predict the SNR based on environmental data. This innovative technique opens up new possibilities for efficient resource allocation, proactive network optimization, and seamless connectivity in dynamic wireless environments, without the constraints imposed by feedback channel availability.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
- Creative Commons Copyright License
The journal allows readers to download and share all published articles as long as they properly cite such articles; however, they cannot change them or use them commercially. This is classified as CC BY-NC-ND for the creative commons license.
- Retention of Copyright and Publishing Rights
The journal allows the authors of the published articles to hold copyrights and publishing rights without restrictions.
References
K. Saija, S. Nethi, S. Chaudhuri and R. M. Karthik,”A Machine Learning Approach for SNR Prediction in 5G Systems,” 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems(ANTS), Goa, India, 2019, pp.1-6.
P. Kazemi, H. Al-Tous, T. Ponnada, C. Studer and O. Tirkkonen, ”Beam SNR Prediction Using Channel Charting,” in IEEE Transactions on Vehicular Technology, vol. 72, no. 10, pp. 13130-13145, Oct. 2023.
C. Beard, W. Stallings and M.P. Tahiliani, Wireless Communication Networks and Systems, Pearson, 2015.
H. Peng, C. Feng, Q. Li, ”User State Prediction Based on Channel Quality Feedback in 5G Broadcast,” 2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Qinhuangdao, China, 2023, pp. 547-551.
T. S. Rappaport, Wireless Communications: Principles and Practice, 2nd ed., Prentice Hall, 2001.
S. Haykin, Communication Systems, 5th ed., John Wiley & Sons, Inc., 2009.
P. Kazemi, T. Ponnada, H. Al-Tous, Y. -C. Liang and O. Tirkkonen, ”Channel Charting Based Beam SNR Prediction,” 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Porto, Portugal, 2021, pp. 72-77.
Q. Zhou, W. Jiang, D. Wang, and H. D. Schotten, ”Deep Learning-Based Signal-to-Noise Ratio
Prediction for Realistic Wireless Communication,” 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland, 2022, pp. 1-5.
P. M. and B. R. Reddy, ”Machine Learning based Prediction for Channel Stability in a 5G Network,” 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon), Vijaypur, India, 2022, pp. 1-4.
Z. Zhao, B. Deng, F. N. Khan, and H. Y. Fu, ”Deep Learning-Based Signal-to-Noise Ratio Estimation for Underwater Optical Wireless Communication,” 2022 IEEE 14th International Conference on Advanced Infocomm Technology (ICAIT), Chongqing, China, 2022, pp. 120-123.
T. Ngo, B. Kelley, and P. Rad, ”Deep Learning Based Prediction of Signal-to-Noise Ratio (SNR) for LTE and 5G Systems,” 2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM), Reims, France, 2020, pp. 1-6.
C. M. M. Cardoso, F. J. B. Barros, J. A. R. Carvalho, A. A. Machado, H. A. O. Cruz, N. M. C. Alcantara,Applications in IoT Networks Based on Measurements,” Sensors, vol. 22, no. 14, p.5233, 2022.
M. Ransah, R. A. Sowah, J. Meli-Segu, F. A. Katsriku, X. Vilajosana, and W. Owusu-Banahene,
”Characterizing Foliage Influence on LoRaWAN Pathloss in a Tropical Vegetative Environment,”
IET Wireless Sensor Systems, vol. 10, no. 5, pp. 198-207, 2020.
N. Jeftenic, M. Simic, and Z. Stamenkovic, ”Impact of Environmental Parameters on SNR and RSSI in LoRaWAN,” International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey, 2020, pp. 1-6.
Gonzalez-Palacio, M., Tobón-Vallejo, D., Sepúlveda-Cano, L., Roa Pérez, S., Pau, G.,
Le, L., ”LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects,” Data, vol. 8, no. 4, 2022.
N. S. Bezerra, C. Åhlund, S. Saguna, V. A. Jr. de Sousa, ”Temperature Impact in LoRaWAN: A Case Study in Northern Sweden,” Sensors, vol. 19, p.4414, 2019.
S. Calvert, E. Popovici, and P. Leahy, ”Using Environmental Data-based Communication Protocol for Improved Quality of Service in LoRaWAN Applications,” The 32nd Irish Signals and Systems Conference (ISSC), Athlone, Ireland,2021, pp. 1-6.
L. Parri, S. Parrino, G. Peruzzi, and A. Pozzebon, ”Offshore LoRaWAN Networking: Transmission Performances Analysis Under Different Environmental Conditions,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-10, 2021.
E. Goldoni, P. Savazzi, L. Favalli, A. Vizziello, ”Correlation Between Weather and Signal Strength in LoRaWAN Networks: An Extensive Dataset,” Computer Networks, vol. 202, 2022.
L. Breiman, ”Random Forests,” Machine Learning, vol. 45, pp. 5-32, 2001.
T. Chen and C. Guestrin, ”XGBoost: A Scalable Tree Boosting System,” in Proceedings of the
nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16), Association for Computing Machinery, New York, NY, USA, 2016, pp. 785-794.
L. Prokhorenkova, G. Gusev, A. Vorobev, A. Dorogush, and A. Gulin, ”CatBoost: Unbiased
Boosting with Categorical Features,” In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18), canada, USA, 2018, pp. 6639-6649.
