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International Journal of Advanced Networking and Applications

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2022
2023

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Ituabhor, Odesanya

Achievable Throughput over Mobile Broadband Network Protocol Layers: Practical Measurements

Abstract Views :107 | PDF Views:0

Authors

Isabona Joseph ¹, Odesanya Ituabhor ¹, Jangfa Timothy zhimwang ¹, Risi Ikechi ²

Affiliations
1 Department of Physics, Federal University Lokoja, Lokoja, Kogi State, NG
2 Department of Physics, IgnatusAjuru University of Eduction, Port Harcourt, NG

Source

International Journal of Advanced Networking and Applications, Vol 13, No 4 (2022), Pagination: 5037-5044

Abstract

Having cognizant practical knowledge of the achievable throughput quality that can be attained at the user equipment communication device over deployed mobile broad cellular networks radio interface under different protocol layers has manifold gains for both subscriber and the radio network service operators. The benefits range from root-cause analysis of results, the network status exposure, triggering of performance optimisation, to path and server selection. However, most radio network service operators usually hide this information regarding practical performance status their networks. To tackle this problem, a practical–based measurement assessment and in-depth analysis of cellular network performance status is proposed. To actualize this, firstly, radio frequency eNodeB site survey was conducted to identify accessible field drive test routes and obtained the relevant eNodeB transmitting engineering parameters, Secondly, an in depth radio channel and achievable throughput quality and radio channel has been conducted under PDCP, RLC and PDSCH LTE protocol layers. Thirdly, the impact user communication distance, signal quality and signal coverage levels on the achievable throughput quality is reported across for three different eNodeB transceivers. The results at PDCP, RLC and PDSCH layers indicate that the achievable throughput degrades proportionally with user location transmission distance and radio network signal coverage and signal quality conditions.

Keywords

Practical Measurements, Achievable Throughput, RSRP, RSRQ, Transmission Distance.

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References

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. 3GPP TS 23.322, UTRAN- and E-UTRAN-based systems ―Radio Link Control Protocol Specification‖ (Release 8)

. 3GPP TS 23.323, UTRAN- and E-UTRAN-based systems ―Packet Data Coverage Protocol Specification‖ (Release 8)

. S. Sesia , I. Toufik, M. Baker,‖LTE The UMTS Long Term Evolution from theory to practice‖, 2009 John Wiley & Sons Ltd.

. S. Kozo, T. Eiji, O. Take, ,Takayuki S,DaisukeOhta,KoseiK,andTutomu M, Passive Method for Estimating Available Throughput for Autonomous OffPeak Data Transfer,Hindawi Wireless Communications and Mobile Computing, Vol. 2020,1-12,.

. J. IsabonaMean User Data Throughput Assessment from a Public UMTS/HSPA Network, Recent Trends in Electronic and Communication Systems, Vol. 2. No 2, 2015, 1-6.

. J. Isabona, and E, A Odion, Quantification of link Average Throughput Performance in HSDPA and HSUPA by Field Measurement, Computing, Information Systems, Development Informatics and Allied Research Journal, Vol. 6, No 1, 49-60, 2014.

. J. Isabona, J, and D.O. Ojuh, Radio Link Quality measurement Survey over HSDPA Radio Interface: A Holistic Technique for Efficient Networks Performance Monitoring in Wireless Communication Systems, Nigerian journal of Physics, Vol. 25 (2), 46-54, 2016.

. J. Isabona, and A.S. Olayinka, Samson‘‘Experimental Investigation of Throughput Performance of IEEE 802.11g OFDM based Systems in a Campus Environment’’, International Journal of Engineering Sciences, vol.2, issue 8, 427-434, 2013.

Y. Gao, D. Chiu and J. Lui, “Determining the End-toEnd Throughput Capacity in Multi-hop Networks: Methodology and Applications”, in Proceedings of the Joint International Conference on Measurement and Modeling of Computer Systems, pp. 39- 50, 2006.

M. Goutelle and P. Primet, “Study of a Non-intrusive Method for Measuring the End-to-End Capacity and useful Bandwidth of a Path”, in Proceedings of IEEE International Conference on Communications, pp. 18721876, 2004.

A. Johnsson, B. Melander and M. Bjorkman, “An Analysis of Active End-to-End Bandwidth Measurements in Wireless Networks”, in Proceedings of IEEE Workshop on End to End Monitoring Techniques and Services, pp. 74-81, 2006.

. F. Rezaei, M. Hempel, and H. Sharif, "LTE PHY Performance Analysis under 3GPP Standards Parameters" International workshop on Computer aided modelling and Design of Communication Links and Networks (CAMAD), pp. 102-106, 2011.

. J. Isabona, ‘‘Advances in MIMO Antenna Systems for Next Generation Mobile Broadband Networks: Achievable Sum Rates and Energy Efficiency, Advance Academic Publisher, ArajiBhupatpatti, Post- Muradganj, Jaunpur-222001 Uttar Pradesh, India, 2018.

. Joseph, I. Joint Statistical and Machine Learning Approach for Practical Data-Driven Assessment of User Throughput Quality in Microcellular Radio Networks. Wireless PersCommun (2021). https://doi.org/10.1007/s11277-021-08300-x

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

Abstract Views :46 | PDF Views:1

Authors

Isabona Joseph ¹, Odesanya Ituabhor ², Emughedi Oghu ³, Omasheye Okiemute Roberts ⁴

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

Source

International Journal of Advanced Networking and Applications, Vol 15, No 2 (2023), Pagination: 5892-5907

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

Full Text

References

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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

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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

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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.

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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.

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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.

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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

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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

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