Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network
Source abstract
Abstract
In present scenario of wireless communications, Long Term Evolution (LTE) based network technology is evolved and provides consistent data delivery with high speed and minimal delay through mobile devices. The traffic management and effective utilization of network resources are the key factors of LTE models. Moreover, there are some major issues in LTE that are to be considered are effective load scheduling and traffic management. Through LTE is a depraved technology, it is been suffering from these issues. On addressing that, this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing (SMO-ELB). In this model, load computation of each mobile node is done with Bounding Theory based Load derivations and optimal cell selection for seamless communication is processed with Spider Monkey Optimization Algorithm. The simulation results show that the proposed model provides better results than exiting works in terms of efficiency, packet delivery ratio, Call Dropping Ratio (CDR) and Call Blocking Ratio (CBR).
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced MIMO Systems Optimization · Advanced Wireless Network Optimization · Cooperative Communication and Network Coding
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References (27)
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1. J. M. Meredith, “3gpp technical specification group radio access network-study on downlink multiuser superposition transmission (must) for lte (release 13),” Online Document, 3GPP, Technical Report, 2015.
2. Long term evolution (LTE): A technical overview
3. LTE: The evolution of mobile broadband10.1109/MCOM.2009.4907406 ↗
4. Scheduling algorithms for 3gpp long-term evolution systems: From a quality of service perspective
5. Performance analysis of EXP/PF and M-LWDF in downlink 3GPP LTE system
6. Adaptive proportional fair scheduling in multihop ofdma systems
7. Comparison of different network densification alternatives from the lte downlink performance point of view
8. Optimization of pilot power for load balancing in WCDMA networks
9. WCDMA common pilot power control for load and coverage balancing
10. Load balancing in WCDMA systems by adjusting pilot power
11. 3GPP TR 36.902, “Self-configuring and self-optimizing network (SON) use cases and solutions,” Technical report (TR), V9.2.0, 2010.
12. Load balancing in downlink lte self-optimizing networks
13. On mobility load balancing for lte systems
14. Conflict avoidance between mobility robustness optimization and mobility load balancing
15. Traffic steering by self-tuning controllers in enterprise LTE femtocells10.1186/1687-1499-2012-337 ↗
16. The use of mobile positioning supported traffic density measurements to assist load balancing methods based on adaptive cell sizing
17. User's mobility history-based mobility prediction in LTE femtocells network
18. Prediction based RSS fingerprinting for positioning and optimization in cellular networks
19. A Markov-based framework for handover optimization in hetnets
20. Handover prediction for long-term window scheduling based on SINR maps
21. Dynamic power control for balanced data traffic with coverage in femtocell networks
22. A method of fingerprint indoor localization based on received signal strength difference by using compressive sensing10.1186/s13638-020-01683-8 ↗
23. A load balancing method in downlink LTE network based on load vector minimization
24. Dynamic analysis of load balancing algorithms in LTE/LTE-a HETNETS10.1007/s11277-017-4089-3 ↗
25. Cell selection for load balancing in heterogeneous networks10.1007/s11277-018-5689-2 ↗
26. Optimization of user behavior based handover using fuzzy Q-learning for LTE networks10.1007/s11276-016-1348-2 ↗
27. Load balancing and handover joint optimization in LTE networks using fuzzy logic and reinforcement learning10.1016/j.comnet.2014.10.027 ↗