Analysis and Modeling of Intelligent Channel Allocation Techniques in Mobile Communication Systems
Abstract
This study is on the analysis and modeling of intelligent channel allocation techniques in mobile communication systems where different channel allocation techniques were considered in mobile communication systems. Also, the different channels which are used in the cellular networks were sufficiently described. Various parameters in mobile communication system that are related to channel allocation techniques and blocked calls were identified and discussed. Data collection was carried out from the Network Switching Center of a highly established and reputable Telecommunication Operator. Data were collected both from the Base Transceiver Stations (BTS), the Base Station Controller (BSC) and the Mobile Switching Centre (MSC), at different locations in Nigeria. These data were collected at different times on monthly basis for a period of thirty six (36) months in the course of this study. Proteus software was used to analyze data collected to enhance the simulation set up. A model (GRACE MODEL) of intelligent channel allocation technique algorithm was developed to reduce the call blocking and also call dropping probability. To authenticate the effectiveness of the proposed algorithm, the simulation result was demonstrated respectively showing normal capacity; over capacity; rerouting to neighbouring sector, e.t.c. It was observed that with the use of the proposed channel re-assignment strategies algorithm, the radio coverage at large distances had more increase in the system channel capacity. It was also observed that the blocked calls (the chance that a call will have no service) were highly reduced due to intelligent philosophy in the developed model. Additionally, the model helped to scale up performance and provided great improvement in network spectral and energy efficiency.
Downloads
References
Alam et al. (2017). Cognitive Radio Based Smart Grid Communication Network. Renew Sustainable Energy Revolution ELSEVIER, 72(1). 535-548.
Azarhava, H.; Pourmohammad, M. and Mosevi, N. (2020). Age of Information in Wireless Powered IoT Networks: NOMA vs TDMA. Ad Hoc Networks, 104 (102179), 10-21.
Huyn, et al. (2015). Interference Avoidance for Smart Metering System by Optimization Assignment Problem. 7th internayional IEEEConference on New Technology, Mobility and Security 2(3), 1-5.
Khan, A. et al. (2016). Cognitive Radio for Smart Grids : Survey of Architectures, Spectrum Sensing Mechanism and Networking Protocols. IEEE Communication Surveys, 18(1), 860-898.
Kosta, A.; Pappas, N. and Angelakis, V. (2017). Age of Information in a New Concept, Metric and Tool. Foundation and Trends in Networking, 12 (3 ) 162-259.
Miao et al. (2015). A Heuristic Best-fit Clustering Approach for Remote Metering Smart Grid Networks. Advancing in Power System Control 10th International Conference on Operation and Management.1(2), 1-10.
Vrbsky et al. (2017). Clustering Techniques for Data Network Planning in Smart Grids. IEEE 14th International Confereence on Network Sensing and Control, 2(1) 7-12.
Yang et al. (2016). Dynamic Spectrum Allocation Algorithm Based on Fairness for Smart Grid Communication Networks. 35th IEEE Chinese Control Conference, 1(2), 6873-6877.
Zhang, Z.; Xiao, Y.; Ma, Z.; Xiao, M.; Ding, Z.; Lei, X.; Karagiannidis, G.K. and Fan, P.(2019). 6G Wireless Networks: Vision, Requirements, Architecture and Key
The authors and co-authors warrant that the article is their original work, does not infringe any copyright, and has not been published elsewhere. By submitting the article to GPH-International Journal of Computer Science and Engineering (GPH-IJCSE), the authors agree that the journal has the right to retract or remove the article in case of proven ethical misconduct.
