International Journal of Advanced Networking and Applications

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Path Loss and Models: A Survey and Future Perspective for Wireless Communication Networks


Affiliations
1 Department of Physics, Federal University Lokoja, Lokoja, Kogi State, Nigeria
2 Federal University Lokoja/Department of Physics, Lokoja, Nigeria
3 Department of Computer Science, Federal University Lokoja, Nigeria
4 Department of Physics, Delta State College of Education, Mosogar 331101, Nigeria
 

Modern wireless systems for mobile communication use electromagnetic waves to transmit information over the air, enabling seamless connectivity for a wide range of devices. However, one of the key challenges in wireless communication paths is loss in the strength of propagated signals. Path loss refers to the reduction in signal strength as it propagates through the wireless channel. Path loss models are mathematical representations that capture the attenuation of signal power due to various factors such as distance, frequency, obstacles, and environmental conditions. Understanding and modeling path loss is crucial for designing and optimizing wireless communication systems, as it directly impacts the coverage area, link quality, and overall performance of the network. By accurately modeling path loss, engineers can also optimize various aspects of a wireless communication system, such as antenna placement; transmit power control, and interference mitigation, ultimately improving the broad-spectrum performance and reliability of the network. This paper investigates the concept of path loss in wireless communication networks and provides a comprehensive overview of its various models and their use in designing and implementation of networks. Furthermore, it reviews existing path loss models, and explains their advantages and disadvantages. Finally, it discusses the current trends future research directions related to path loss and its models. The findings in this study can help them better design and implement robust wireless communication networks with improved signal quality and capacity.

Keywords

Propagated Signals, network performance, fading, Path loss, path loss modeling, Model optimization
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  • I. O. Ann, O. Itanyi, and I. U. Wilfred, “Managing innovations in telecommunications industry in Nigeria,” African J. Bus. Manag., vol. 6, no. 25, pp. 7469–7477, 2012.

  • J Isabona, AL Imoize, S Ojo, I Risi Optimal Call Failure Rates Modelling with Joint Support Vector Machine and Discrete Wavelet Transform International Journal of Image, Graphics and Signal Processing, vol. 14 (4), 46- 57.2022.

  • Olukanni, Seyi E, Isabona Joseph and Odesanya, Ituabhor, Enhancing Lte Rss for a Robust Path Loss Analysis with Noise Removal, International Journal of Image, Graphics and Signal Processing, Vol. 15, Issue 3, pp. 60 – 68, Jun 2023.doi: 10.5815/ijigsp.2023.03.05

  • Omasheye OR, Azi S, Isabona J, Imoize AL, Li C-T, Lee C-C. Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks. Future Internet. 2022; 14(12):373. https://doi.org/10.3390/fi14120373

  • Theodore, S. Wireless Communications: Principles and Practice, 2nd ed.; Pearson: Paris, France, 2002; 640p.

  • Alnatoor, M., Omari, M., & Kaddi, M. (2022). Path Loss Models for Cellular Mobile Networks Using Artificial Intelligence Technologies in Different Environments.Applied Sciences, 12(24), 12757 https://doi.org/10.3390/app122412757

  • Isabona, J., Srivastava, V.M. Coverage and Link Quality Trends in Suburban Mobile Broadband HSPA Network Environments. Wireless Pers Commun 95, 3955–3968 (2017). https://doi.org/10.1007/s11277-017-4034-5.

  • Divine O. Ojuh, Joseph Isabona, Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band, International Journal of Intelligent Systems and Applications(IJISA), Vol.13, No.4, pp.14-23, 2021. DOI: 10.5815/ijisa.2021.04.02

  • J. Isabona et al., “Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments,” Applied Sciences, vol. 12, no. 11. p. 5713, 2022, doi: 10.3390/app12115713.

  • Ogbonda, C., Isabona, J., & Olukanni, S. E., A Wavelet Genetic Algorithm Based Signal Path Loss Model for Evolved Nodeb Cell Sites Planning And Effective Coverage of Wireless Communication System, International Journal of Innovations in Engineering, Science and Technology, Vol. 12, Number 2, 2023. https://www.bwjournal.org/index.php/bsjournal/ article/view/1279

  • Liming X, Dacheng Y (2003) “A recursive algorithm for radio propagation model calibration based on CDMA forward pilot channel,” in 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 1:970–972. https://doi.org/10.1109/PIMRC.2003.1264418

  • Zrni VP, Akademija KA, Resanovi R (2001) Minimax LS algorithm for automatic propagation model tuning, Proceeding of the 9th Telecommunications Forum, Belgrade, Serbia Nov. 20-22, 2001, 1-5.

  • Isabona Joseph，and Divine O. Ojuh,; Adaptation of Propagation Model Parameters toward Efficient Cellular Network Planning using Robust LAD Algorithm, International Journal of Wireless and Microwave Technologies,Vol.10, No.5, pp. 13-24, 2020.DOI: 10.5815/ijwmt.2020.05.02.

  • Abhayawardhana, V.S.; Wassell, I.J.; Crosby, D.; Sellars, M.P.; Brown, M.G. Comparison of empirical propagation path loss models for fixed wireless access systems. In Proceedings of the 2005 IEEE 61st Vehicular Technology Conference, Stockholm, Sweden, 30 May–1 June 2005.

  • Isabona, J., Imoize, A.L. Terrain-based adaption of propagation model loss parameters using non-linear square regression. J. Eng. Appl. Sci. 68, 33 (2021). https://doi.org/10.1186/s44147- 021-00035-7.

  • Joseph Isabona, Divine O. Ojuh, Application of Levenberg- Marguardt Algorithm for Prime Radio Propagation Wave Attenuation Modelling in Typical Urban, Suburban and Rural Terrains, International Journal of Intelligent Systems and Applications (IJISA), Vol.13, No.3, pp.35-42, 2021. doi:10.5815/ijisa.2021.03.04

  • Seyi E. Olukanni, Joseph Isabona, ItuabhorOdesanya, "Radio Spectrum Measurement Modeling and Prediction based on Adaptive Hybrid Model for Optimal Network Planning", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.4, pp. 19-32, 2023. DOI:10.5815/ijigsp.2023.04.02

  • Ebhota, V.C, Isabona, J, and Srivastava, V.M. ‘‘Effect of Learning Rate on GRNN and MLP for the prediction of Approximation Signal Power Loss in Microcell Sub-Urban Environment’’, International Journal on Communications Antenna and Propagation Vol. 9 (1), pp. 36-45, 2019.

  • J. Isabona, A. L. Imoize, and Y. Kim, “Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning,” Sensors, vol. 22, no. 10, p. 3776, May 2022, doi: 10.3390/s22103776.

  • Risi, I., Ogbonda, C., Joseph, I. (2023). Development and Comparative Analysis of Path Loss Models Using Hybrid Wavelet-Genetic Algorithm Approach. In: Hu, Z., Zhang, Q., He, M. (eds) Advances in Artificial Systems for Logistics Engineering III. ICAILE 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 180. Springer, Cham. https://doi.org/10.1007/978-3- 031-36115-9_45.

  • A. Akinbolati and M. O. Ajewole, “Investigation of path loss and modeling for digital terrestrial television over Nigeria,” Heliyon, vol. 6, no. 6, p. e04101, 2020, doi: https://doi.org/10.1016/j.heliyon.2020.e04101.

  • A. L. Imoize, S. O. Tofade, G. U. Ughegbe, F. I. Anyasi, and J. Isabona, “Updating analysis of key performance indicators of 4G LTE network with the prediction of missing values of critical network parameters based on experimental data from a dense urban environment,” Data Br., p. 108240, 2022, doi: https://doi.org/10.1016/j.dib.2022.108240.

  • A. L. Imoize, F. Udeji, J. Isabona, and C.-C. Lee, “Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment,” Future Internet, vol. 15, no. 5. 2023. doi: 10.3390/fi15050181.

  • J. Isabona, “Joint Statistical and Machine Learning Approach for Practical Data-Driven Assessment of User Throughput Quality in Microcellular Radio Networks,” Wirel. Pers.Commun., vol. 119, no. 2, pp. 1661–1680, 2021.

  • V. C. Ebhota, J. Isabona, and V. M. Srivastava, “‘Effect of Learning Rate on GRNN and MLP for the Prediction of Signal Power Loss in Microcell Sub-Urban Environment,” Int.J. Commun. Antenna Propag., vol. 9, no. 1, pp. 36–45, 2019.

  • K. Obahiagbon and J. Isabona, “‘Generalized Regression Neural Network: an Alternative Approach for Reliable Prognostic Analysis of Spatial Signal Power Loss in Cellular Broadband Networks,” Int. J. Adv. Res. Phys. Sci., vol. 5, no. 10, pp. 35–42, 2018.

  • H. Tataria, K. Haneda, A. F. Molisch, M. Shafi, and F. Tufvesson, “Standardization of Propagation Models: 800 MHz to 100 GHz -- A Historical Perspective,” 2020, [Online].Available: http://arxiv.org/abs/2006.08491.

  • Isabona Joseph, Ibitome Lanlege Louis, Imoize Agbotiname Lucky, Mamodiya Udit, Kumar Ankit, Montaser M. Hassan, and Boakye Isaac Kweku, Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks, Computational Intelligence and Neuroscience, Hindawi, volume. 2023.https://doi.org/10.1155/2023/5236566

  • J. Isabona and D. O. Ojuh, “Application of Levenberg-Marguardt Algorithm for Prime Radio Propagation Wave Attenuation Modelling in Typical Urban, Suburban and Rural Terrains,” Int. J. Intell. Syst. Appl., vol. 13, no. 3, pp. 35–42, 2021.

  • D. O. Ojuh and J. Isabona, “Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band,” Int. J. Intell. Syst. Appl., vol. 13, no. 4, 2021.

  • Michael Atenaga and Joseph Isabona, On The Compromise between Network Performance and End User Satisfaction over UMTS Radio Interface: An Empirical Investigation, International Journal of Advanced Research in Physical Science (IJARPS) Volume 1, Issue 8, PP 9-18, December 2014.

  • I. Joseph and C. C. Konyeha, “Urban area path loss propagation prediction and optimisation using Hata model at 800MHz,” IOSR J. Appl. Phys., vol. 3, no. 4, pp. 8–18, 2013.

  • V. C. Ebhota, J. Isabona, and V. M. Srivastava, “Environment-Adaptation Based Hybrid Neural Network Predictor for Signal Propagation Loss Prediction in Cluttered and Open Urban Microcells,” Wirel. Pers. Commun., vol. 104, no. 3, pp. 935–948, 2019, doi:10.1007/s11277- 018-6061-2.

  • Isabona Joseph, Konyeha. C. C, Chinule. C. Bright, ,Isaiah Gregory Peter, Radio field strength propagation data and pathloss calculation methods in UMTS network,” Advances in Physics Theories and Applications, vol. 21, pp. 54–68, 2013.

  • Isabona, J.; Srivastava, V.M. Hybrid neural network approach for predicting signal propagation loss in urban microcells. In Proceedings of the 2016 IEEE Region 10 Humanitarian Technology Conference (R10- HTC), Agra, India, 21–23 December 2016; pp. 1–5.

  • L. C. Fernandes and A. J. M. Soares, “Path loss prediction in microcellular environments at 900MHz,” AEU - Int. J. Electron. Commun., vol. 68, no. 10, pp. 983–989, 2014, doi:https://doi.org/10.1016/j.aeue.2014.04.020.

  • Isabona, J and Peter I. G. (2014), ‘‘Benchmarking Mobile Network Quality of Service with Essential Key Performance Indicators: A Case Study of Operational GSM Telecom Operators in Nigeria. Conference Proceedings of Nigerian Institute of Physics, 2014.

  • J. Isabona and I. G. Peter, “CDMA2000 radio measurements at 1.9 GHz and comparison of propagation models in three built-up cities of SouthSouth-South, Nigeria,” Am. J. Eng. Res, vol. 2, no. 05, pp. 96–106, 2013

  • Ebhota, V.C, Isabona, J, and Srivastava, V.M. (2019), Environment-Adaptation Based Hybrid Neural Network Predictor for Signal Propagation Loss Prediction in Cluttered and Open Urban Microcells, Wireless Personal Communications, Vol. 104 (3), pp. 935–948.

  • Isabona, J and Agbotiname Lucky Imoize, (2021), ‘‘Terrain-based Adaption of Propagation Model Loss Parameters using Nonlinear Square Regression, Journal of Engineering and Applied Science (Springer), (2021) 68:33

  • Isabona Joseph, Ibrahim Habibat Ojochogwu and Ituabhor Odesanya Tropical Rain Intensity Impact on Raindrop Diameter and Specific Signal Attenuation at Microwaves Communication Link, I.J. Image, Graphics and Signal Processing, vol.15, Issue 2, pp.59-72, April 8, 2023. DOI: 10.5815/ijigsp.2023.02.06

  • J. Isabona et al., “Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments,” Applied Sciences, vol. 12, no. 11. p. 5713, 2022, doi: 10.3390/app12115713.

  • J. Isabona and K. Obahiagbon, “RF propagation measurement and modelling to support adept planning of outdoor wireless local area networks in 2.4 GHz Band,” Am. J. Eng. Res., vol. 3, no. 1, pp. 258–267, 2014.

  • SE Olukanni, J Isabona, I Odesanya, AL Imoize, CC Lee, Adaptive Tuning of the Logdistance Model for Optimal Predictive Modeling of Pathloss over Irregular Terrains, International Journal of Computing and Digital Systems 14 (1), 1-xx, https://journal.uob.edu.bh/bitstream/handle/123 456789/5106/1570870091.pdf?sequence=1

  • Pedraza, L.F.; Hernández, C.A.; López, D.A. A model to determine the propagation losses based on the integration of hata-okumura and wavelet neural models. Int. J. Antennas Propag. 2017, 2017, 1–8.

  • Z. Huang, X. Cheng, and N. Zhang, “An improved non-geometrical stochastic model for non-WSSUS vehicle-to-vehicle channels,” ZTE Commun., vol. 17, no. 4, p. 62, 2019.

  • . Nwelih, E, Isabona, J and Imoize, A.L. Optimisation of base Station Placement in 4G LTE Broadband Networks uing Adaptive Variable Length Genetic Algorithm, SN Computer Science, vol.4, 121 (2023). https://doi.org/10.1007/s42979-022-01533-y

  • D. He, B. Ai, K. Guan, L. Wang, Z. Zhong, and T. Kürner, “The design and applications of high-performance ray-tracing simulation platform for 5G and beyond wireless communications: A tutorial,” IEEE Commun. Surv. Tutorials, vol. 21, no. 1, pp. 10–27, 2018.

  • K. Chamberlin and R. Luebbers, “An evaluation of Longley-Rice and GTD propagation models,” IEEE Trans. Antennas Propag., vol. 30, no. 6, pp. 1093–1098, 1982.

  • J. B. Andersen, “UTD multiple-edge transition zone diffraction,” IEEE Trans. Antennas Propag., vol. 45, no. 7, pp. 1093–1097, 1997.

  • M. Hata, “Empirical Formula for Propagation Loss in Land Mobile Radio Services,” IEEE Trans. Veh. Technol., vol. 29, no. 3, pp. 317– 325, 1980, doi: 10.1109/T-VT.1980.23859.

  • Joseph Isabona, Agbotiname Lucky Imoize, Stephen Ojo, Prashanth Venkatareddy, Simon Karanja Hinga, Manuel Sanchez-Chero, Sheda Méndez Ancca, Accurate Base Station Placement in 4G LTE Networks Using Multiobjective Genetic Algorithm Optimization, Wireless Communications and Mobile Computing, 2023, 1-9 February 2023, https://doi.org/10.1155/2023/7476736

  • Ekpenyong, M.; Isabona, J.; Ekong, E. On Propagation Path Loss Models For 3-G Based Wireless Networks: A Comparative Analysis. Comput. Sci. Telecommun. 2010, 25, 74–84.

  • Edith Edimo Joseph, Joseph Isabona, Odaro Osayande, Ikechi Irisi, "Optimised MLP Neural Network Model for Optimum Prognostic Learning of out of School Children Trend in Africa: Implication for Guidance and Counselling", International Journal of Modern Education and Computer Science(IJMECS), Vol.15, No.1, pp. 1-12, 2023. DOI:10.5815/ijmecs.2023.01.01.

  • D.O. Ojuh, and J. Isabona, (2021), Empirical and Statistical Determination of Optimal Distribution Model for Radio Frequency Mobile Networks Using Realistic Weekly Block Call Rates Indicator, I. J. Mathematical Sciences and Computing, 2021, 3, 12-23.

  • J. Isabona and V. M. Srivastava, “Coverage and Link Quality Trends in Suburban Mobile Broadband HSPA Network Environments,” Wirel. Pers. Commun., vol. 95, no. 4, pp. 3955– 3968, 2017, doi: 10.1007/s11277-017-4034-5.

  • Isabona, J. Joint Statistical and Machine Learning Approach for Practical Data-Driven Assessment of userBroadband Throughput Quality in Microcellular Radio Networks. Wirel. Pers. Commun. 2021, 119, 1661–1680.

  • J. Zhang, C. Gentile, and W. Garey, “On the cross-application of calibrated pathloss models using area features: finding a way to determine similarity between areas,” IEEE Antennas Propag. Mag., vol. 62, no. 1, pp. 40–50, 2019.

  • M. A. Amanaf, A. Hikmaturokhman, and A. F. Septian, “Calibrating the Standard Propagation Model (SPM) for Suburban Environments Using 4G LTE Field Measurement Study Case in Indonesia,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 982, no. 1, p. 12029.

  • M. A. Adelabu, A. A. Ayorinde, H. A. Muhammed, F. O. Okewole, and I. Mowete, “A Quasi-Moment-Method-Based Calibration of Basic Pathloss Models,” Elektr. Electr. Eng., vol. 19, no. 3, pp. 35–48, 2020.

  • M. A. Adelabu, A. Ayorinde, and A. I. Mowete, “Prediction Characteristics of Quasi-Moment- Method Calibrated Pathloss Models,” Int. J. Comput. Appl., vol. 975, p. 8887, 2020.

  • J. M. Kelner, M. Kryk, J. Łopatka, and P. Gajewski, “A statistical calibration method of propagation prediction model based on measurement results,” Int. J. Electron. Telecommun., vol. 66, 2020.

  • C. Phillips, D. Sicker, and D. Grunwald, “Bounding the practical error of path loss models,” Int. J. Antennas Propag., vol. 2012, 2012.

  • J. Isabona and A. L. Imoize, “Terrain-based adaption of propagation model loss parameters using non-linear square regression,” J. Eng. Appl. Sci., vol. 68, no. 1, pp. 1–19, 2021.

  • L. Akhoondzadeh-Asl and N. Noori, “Modification and Tuning of the Universal Okumura-Hata Model for Radio Wave Propagation Predictions,” in 2007 Asia-Pacific Microwave Conference, 2007, pp. 1–4, doi: 10.1109/APMC.2007.4554925.

  • Z. Nadir and M. I. Ahmad, “Pathloss determination using Okumura-Hata model and cubic regression for missing data for Oman,” Proc. Int. MultiConference Eng. Comput. Sci. 2010, IMECS 2010, no. March, pp. 804–807, 2010.

  • A. Akinbolati and M. O. Ajewole, “Investigation of path loss and modeling for digital terrestrial television over Nigeria,” Heliyon, vol. 6, no. 6, p. e04101, 2020, doi: https://doi.org/10.1016/j.heliyon.2020.e04101.

  • T. Jawhly and R. C. Tiwari, “The special case of Egli and Hata model optimization using least-square approximation method,” SN Appl. Sci., vol. 2, no. 7, pp. 1–10, 2020.

  • B. S. L. Castro, M. R. Pinheiro, G. P. S. Cavalcante, I. R. Gomes, and O. De O Carneiro, “Comparison between known propagation models using least squares tuning algorithm on 5.8 GHz in Amazon region cities,” J. Microwaves Optoelectron., vol. 10, no. 1, pp. 106–113, 2011, doi: 10.1590/S2179- 10742011000100011.

  • D. Castro-Hernandez and R. Paranjape, “Local Tuning of a Site-Specific Propagation Path Loss Model for Microcell Environments,” Wirel. Pers. Commun., vol. 91, no. 2, pp. 709–728, 2016, doi: 10.1007/s11277-016-3489-0.

  • M. Yang and W. Shi, “A Linear Least Square Method of Propagation Model Tuning for 3G Radio Network Planning,” in 2008 Fourth International Conference on Natural Computation, 2008, vol. 5, pp. 150–154, doi: 10.1109/ICNC.2008.188.

  • J. Isabona, “Wavelet Generalized Regression Neural Network Approach for Robust Field Strength Prediction,” Wirel. Pers. Commun., vol. 114, pp. 3635–3653, 2020, [Online]. Available: https://doi.org/10.1007/s11277-020- 07550-5.

  • J. Isabona and A. L. Imoize, “Optimal Kernel Selection Based on GPR for Adaptive Learning of Mean Throughput Rates in LTE Networks,” J. Technol. Adv., vol. 1, no. 1, pp. 1–21, 2021, doi: 10.4018/jta.290350.

  • J. Isabona and D. O. Ojuh, “Application of Levenberg-Marguardt Algorithm for Prime Radio Propagation Wave Attenuation Modelling in Typical Urban, Suburban and Rural Terrains,” Int. J. Intell. Syst. Appl., vol. 13, no. 3, pp. 35–42, 2021.

  • R. Mardeni and L. Y. Pey, “The optimization of Okumura’s model for Code Division Multiple Access (CDMA) system in Malaysia,” Eur. J. Sci. Res., vol. 45, no. 4, pp. 508–528, 2010.

  • E. AarnæS and S. Holm, “Tuning of Empirical Radio Propagation Models Effect of Location Accuracy,” Wirel. Pers. Commun., vol. 30, no. 2, pp. 267–281, 2004, doi: 10.1023/B:WIRE.0000049404.44405.82.

  • Sotiroudis, S.P.; Goudos, S.K.; Gotsis, K.A.; Siakavara, J.N. Application of a Composite Differential Evolution Algorithm in Optimal Neural Network Design for Propagation Path- Loss Prediction in Mobile Communication Systems. IEEE Antennas Wirel. Propag. Lett. 2013, 12, 364–367

  • Jo, H.S.; Park, C.; Lee, E.; Choi, H.K.; Park, J. Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network and Gaussian Process. Sensors 2020, 20, 1927

  • Zhang, Y.; Wen, J.; Yang, G.; He, Z.; Wang, J. Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion. Appl. Sci. 2019, 9, 1908

  • Piacentini, M.; Rinaldi, F. Path loss Prediction in Urban Environment Using Learning Machines and Dimensionality Reduction Techniques. Comput. Manag. Sci. 2011, 8, 371– 385

  • Oroza, C.A.; Zhang, Z.; Watteyne, T.; Glaser, S.D. A Machine-Learning-Based Connectivity Model for Complex Terrain Large-Scale Low- Power Wireless Deployments. IEEE Trans. Cogn. Commun. Netw. 2017, 3, 576–584.


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  • Path Loss and Models: A Survey and Future Perspective for Wireless Communication Networks

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Authors

Isabona Joseph
Department of Physics, Federal University Lokoja, Lokoja, Kogi State, Nigeria
Odesanya Ituabhor
Federal University Lokoja/Department of Physics, Lokoja, Nigeria
Emughedi Oghu
Department of Computer Science, Federal University Lokoja, Nigeria
Omasheye Okiemute Roberts
Department of Physics, Delta State College of Education, Mosogar 331101, Nigeria

Abstract


Modern wireless systems for mobile communication use electromagnetic waves to transmit information over the air, enabling seamless connectivity for a wide range of devices. However, one of the key challenges in wireless communication paths is loss in the strength of propagated signals. Path loss refers to the reduction in signal strength as it propagates through the wireless channel. Path loss models are mathematical representations that capture the attenuation of signal power due to various factors such as distance, frequency, obstacles, and environmental conditions. Understanding and modeling path loss is crucial for designing and optimizing wireless communication systems, as it directly impacts the coverage area, link quality, and overall performance of the network. By accurately modeling path loss, engineers can also optimize various aspects of a wireless communication system, such as antenna placement; transmit power control, and interference mitigation, ultimately improving the broad-spectrum performance and reliability of the network. This paper investigates the concept of path loss in wireless communication networks and provides a comprehensive overview of its various models and their use in designing and implementation of networks. Furthermore, it reviews existing path loss models, and explains their advantages and disadvantages. Finally, it discusses the current trends future research directions related to path loss and its models. The findings in this study can help them better design and implement robust wireless communication networks with improved signal quality and capacity.

Keywords


Propagated Signals, network performance, fading, Path loss, path loss modeling, Model optimization

References