Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks
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Published:
Jul 10, 2024
Keywords:
Algorithm, GERAN, Heterogeneous Wireless Network, Radio Access Technology, Radio Resource Management, UTRAN
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Abstract
In Heterogeneous Wireless Networks (HWNs), Radio Access Technologies (RAT) can only consider the situation of one particular Radio Resource Management (RRM) which is unsuitable for managing multiple RATs. This study deployed an adaptive RAT selection scheme model to allocate users to the best RAT with the use of the cost function variable. The adopted model uses different input criteria like signal strength, network loads, service type and QoS requirement for the best access network selections. The adaptive RAT selection algorithm was executed in different service mixes (voice and data service) to access model suitability for users in Global System for Mobile Communications with Enhanced Data Rates for Global Evolution Radio Access Network (GERAN) and Universal Mobile Telecommunications System Radio Access Network (UTRAN). The proposed algorithm resulted in the call blocking probability reduction by 0.03 for GERAN and 0.14 for UTRAN as validated with the existing algorithm based on load balancing, service-based and priority-based. The drop implied an increased probability of ensuring session stability and high quality of the active service, leading to a high load distribution.
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How to Cite
[1]
F. O. Ariba, F. K. Ojo, and Z. Adeyemo, “Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks”, AJERD, vol. 7, no. 2, pp. 51-60, Jul. 2024.
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References
[1] Alumona, T. L., & Nnaemeka, U. E. (2020). 5G Applications in Heterogeneous Network Issues and Challenges. International Journal of Computer Science and Mobile Computing, 9(9), 94-102.
[2] Weiwei, X. (2021). Load Balancing Selection Method and Simulation in Network Communication Based on AHP-DS Heterogeneous Network Selection Algorithm. Hindawi Complexity, 1-21.
[3] Naeem, N., ELAttar, H. M., & Aboul-Dahab, M. J. S. (2019). An optimized load balance solution for multi-homed host in heterogeneous wireless networks. 19(12), 2773.
[4] Liang, G., Yu, I., Guo, X., & Qin, Y. (2019). Joint access selection and bandwidth allocation algorithm supporting user requirements and preferences in heterogeneous wireless networks, 7(1), 23914-23929.
[5] Shiwei, G. (2021). Load Balancing Algorithm for Heterogeneous Wireless Networks Based on Motion State Estimation. IEEE 9th International Conference on Information, Communication and Networks (ICICN), 175-178.
[6] Altahrawi, M., Abdullah, N. F., & Nordin, R. (2022). Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication. 10, 10261-110284.
[7] Ghatak, G., De Domenico, A., & Coupechoux, M. (2018). Coverage analysis and load balancing in HetNets with millimeter wave multi-RAT small cells. IEEE Trans. Wireless Commun, 17(2), 3154-3169.
[8] Zou, H., & Zheng, M. (2019). A Random-Based Approach to Social Influence Maximization in Human Centered Computing. 5th International Conference, HCC, Čačak, Serbia, 5–7.
[9] Sabbagh, A. A., Braun, R., & Mehran, A. (2014). Intelligent Hybrid Cheapest Cost and Mobility optimization RAT selection approaches for heterogeneous wireless networks. Journal of Networks, 9(2), 1-8.
[10] Lahby, M., Cherkaoui, L., & Adib, A. (2013). An enhanced-TOPSIS based network selection technique for next generation wireless networks. IEEE, 1-5.
[11] Lahby, M., Attioui, A., & Sekkaki, A. (2017). An improved policy for network selection decision based on enhanced-topsis and utility function. 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2175-2180.
[12] Anany, M. G., Elmesalawy, M. M., & El Din, E. S. (2019). A matching game solution for optimal RAT selection in 5G multi-RAT hetnets. IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 1022-1028.
[13] Bukhari, J., & Akkari, N. (2016). QoS-based approach for LTE-WiFi handover. 7th International Conference on Computer Science and Information Technology (CSIT), 1-6.
[14] Caso, G., Alay, Ö., Ferrante, G. C., De Nardis, L., Di Benedetto, M. G., & Brunstrom, A. (2021). User-centric radio access technology selection: A survey of game theory models and multi-agent learning algorithms. IEEE Access, 9, 84417-84464.
[15] Yu, H. W., &. Zhang, B. J. (2018). A heterogeneous network selection algorithm based on network attributes and user preferences. IEEE, 72(1), 68-80.
[16] Yu, H.W., & Zhang, B. J. (2019). A hybrid MADM algorithm based on attribute weight and utility value for heterogeneous network selection. IEEE, 27(01), 756-783.
[17] Caso, G., Alay, Ö., Ferrante, G. C., Luca, D. N., Benedetto, M.G. D., & Brunstrom, A. (2021). User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms. IEEE Access, vol. 9(2), 84417-84464.
[18] Zhu, A., Ma, M., Guo, S., & Yang, Y. (2022). Adaptive access selection algorithm for multi-service in 5G heterogeneous internet of things. IEEE Transactions on Network Science and Engineering, 9(3), 1630-1644.
[19] Carvalho, G. H., Woungang, I., Anpalagan, A., Coutinho, R. W., & Costa, J. C. (2013). A semi-Markov decision process-based joint call admission control for inter-RAT cell re-selection in next generation wireless networks. Computer Networks, 57(17), 3545-3562.
[20] Ahuja, K., Singh, B., & Khanna, R. (2018). Network selection in wireless heterogeneous environment by CPF hybrid algorithm. Wireless Personal Communications, 98, 2733-2751.
[21] Mir, Z. H., Toutouh, J., Filali, F., & Ko, Y. B. (2020). Enabling DSRC and C-V2X integrated hybrid vehicular networks: Architecture and protocol. IEEE access, 8, 180909-180927.
[22] Sivagar, M. R., & Prabakaran, N. (2023). Dynamic LB mechanism using chimp optimisation algorithm in LTE networks. International Journal of System of Systems Engineering, 13(3), 324-345.
[23] SK, O., Idigo, V., Alumona, T., & Okeke, R. J. A., (2019). Simulative Methods of Estimating and Modifying Deployed 4G LTE Network Capacity in Terms of Throughput Performance. Sciences, 15(1), 151-161.
[24] Hossain, M. S., Jahid, A., Islam, K. Z., Alsharif, M. H., Rahman, K. M., Rahman, M. F., & Hossain, M. F. (2020). Towards energy efficient load balancing for sustainable green wireless networks under optimal power supply. IEEE Access, 8, 200635-200654.
[25] Abdallah, A. S., Robin, B., & Abolhasan, M. (2012). A power efficient RAT selection algorithm for heterogeneous wireless networks. presented at the 2012 International Symposium on Communications and Information Technologies (ISCIT), University of Technology, Sydney (UTS) Sydney, Australia.
[26] Mannweiler, C., Sartori, C., Wegmann, B., Flinck, H., Maeder, A., Goerge, J., & Winkelmann, R. (2020). Evolution of mobile communication networks. Towards cognitive autonomous networks: network management automation for 5G and beyond, 29-92.
[2] Weiwei, X. (2021). Load Balancing Selection Method and Simulation in Network Communication Based on AHP-DS Heterogeneous Network Selection Algorithm. Hindawi Complexity, 1-21.
[3] Naeem, N., ELAttar, H. M., & Aboul-Dahab, M. J. S. (2019). An optimized load balance solution for multi-homed host in heterogeneous wireless networks. 19(12), 2773.
[4] Liang, G., Yu, I., Guo, X., & Qin, Y. (2019). Joint access selection and bandwidth allocation algorithm supporting user requirements and preferences in heterogeneous wireless networks, 7(1), 23914-23929.
[5] Shiwei, G. (2021). Load Balancing Algorithm for Heterogeneous Wireless Networks Based on Motion State Estimation. IEEE 9th International Conference on Information, Communication and Networks (ICICN), 175-178.
[6] Altahrawi, M., Abdullah, N. F., & Nordin, R. (2022). Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication. 10, 10261-110284.
[7] Ghatak, G., De Domenico, A., & Coupechoux, M. (2018). Coverage analysis and load balancing in HetNets with millimeter wave multi-RAT small cells. IEEE Trans. Wireless Commun, 17(2), 3154-3169.
[8] Zou, H., & Zheng, M. (2019). A Random-Based Approach to Social Influence Maximization in Human Centered Computing. 5th International Conference, HCC, Čačak, Serbia, 5–7.
[9] Sabbagh, A. A., Braun, R., & Mehran, A. (2014). Intelligent Hybrid Cheapest Cost and Mobility optimization RAT selection approaches for heterogeneous wireless networks. Journal of Networks, 9(2), 1-8.
[10] Lahby, M., Cherkaoui, L., & Adib, A. (2013). An enhanced-TOPSIS based network selection technique for next generation wireless networks. IEEE, 1-5.
[11] Lahby, M., Attioui, A., & Sekkaki, A. (2017). An improved policy for network selection decision based on enhanced-topsis and utility function. 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2175-2180.
[12] Anany, M. G., Elmesalawy, M. M., & El Din, E. S. (2019). A matching game solution for optimal RAT selection in 5G multi-RAT hetnets. IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 1022-1028.
[13] Bukhari, J., & Akkari, N. (2016). QoS-based approach for LTE-WiFi handover. 7th International Conference on Computer Science and Information Technology (CSIT), 1-6.
[14] Caso, G., Alay, Ö., Ferrante, G. C., De Nardis, L., Di Benedetto, M. G., & Brunstrom, A. (2021). User-centric radio access technology selection: A survey of game theory models and multi-agent learning algorithms. IEEE Access, 9, 84417-84464.
[15] Yu, H. W., &. Zhang, B. J. (2018). A heterogeneous network selection algorithm based on network attributes and user preferences. IEEE, 72(1), 68-80.
[16] Yu, H.W., & Zhang, B. J. (2019). A hybrid MADM algorithm based on attribute weight and utility value for heterogeneous network selection. IEEE, 27(01), 756-783.
[17] Caso, G., Alay, Ö., Ferrante, G. C., Luca, D. N., Benedetto, M.G. D., & Brunstrom, A. (2021). User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms. IEEE Access, vol. 9(2), 84417-84464.
[18] Zhu, A., Ma, M., Guo, S., & Yang, Y. (2022). Adaptive access selection algorithm for multi-service in 5G heterogeneous internet of things. IEEE Transactions on Network Science and Engineering, 9(3), 1630-1644.
[19] Carvalho, G. H., Woungang, I., Anpalagan, A., Coutinho, R. W., & Costa, J. C. (2013). A semi-Markov decision process-based joint call admission control for inter-RAT cell re-selection in next generation wireless networks. Computer Networks, 57(17), 3545-3562.
[20] Ahuja, K., Singh, B., & Khanna, R. (2018). Network selection in wireless heterogeneous environment by CPF hybrid algorithm. Wireless Personal Communications, 98, 2733-2751.
[21] Mir, Z. H., Toutouh, J., Filali, F., & Ko, Y. B. (2020). Enabling DSRC and C-V2X integrated hybrid vehicular networks: Architecture and protocol. IEEE access, 8, 180909-180927.
[22] Sivagar, M. R., & Prabakaran, N. (2023). Dynamic LB mechanism using chimp optimisation algorithm in LTE networks. International Journal of System of Systems Engineering, 13(3), 324-345.
[23] SK, O., Idigo, V., Alumona, T., & Okeke, R. J. A., (2019). Simulative Methods of Estimating and Modifying Deployed 4G LTE Network Capacity in Terms of Throughput Performance. Sciences, 15(1), 151-161.
[24] Hossain, M. S., Jahid, A., Islam, K. Z., Alsharif, M. H., Rahman, K. M., Rahman, M. F., & Hossain, M. F. (2020). Towards energy efficient load balancing for sustainable green wireless networks under optimal power supply. IEEE Access, 8, 200635-200654.
[25] Abdallah, A. S., Robin, B., & Abolhasan, M. (2012). A power efficient RAT selection algorithm for heterogeneous wireless networks. presented at the 2012 International Symposium on Communications and Information Technologies (ISCIT), University of Technology, Sydney (UTS) Sydney, Australia.
[26] Mannweiler, C., Sartori, C., Wegmann, B., Flinck, H., Maeder, A., Goerge, J., & Winkelmann, R. (2020). Evolution of mobile communication networks. Towards cognitive autonomous networks: network management automation for 5G and beyond, 29-92.