Details

Autor(en) / Beteiligte

• Debnath, Sanjoy
• Arif, Wasim
• Sen, Debarati
• Baishya, Srimanta

Titel

LTE Cell Planning for Resource Allocation in Emergency Communication

Ist Teil von

• Wireless personal communications, 2024-03, Vol.135 (2), p.1035-1076

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The role of information and communication technology infrastructure is very crucial and perhaps most important during and post disaster (DPD) scenarios where thousands of lives are at risk. Communication services are expected to operate effectively in such demanding situations with restricted resources while fulfilling their core functionalities. The absence of coordinated cell planning taking the vulnerability of the geographical zone into account is a drawback that inhibits system operations and rescue efforts of public protection and disaster relief (PPDR) units. In this paper, the major issues of cell planning are encountered, and new algorithms for optimum LTE cell planning based on the hybrid dragonfly algorithm with differential evolution (DADE) are proposed under user coverage, user association, and capacity constraints. Thereafter, the feasibility of deployment and operation of an operator-independent emergency system (ES) integrated with balloon-based lightweight LTE eNodeB is analyzed to mitigate the DPD communication challenges. Then evaluate the optimal location for the deployment of ESs to cater to the users under the aforementioned constraint. Finally, optimum cell planning considering the vulnerability of the zone is discussed. The comparative comprehensive analysis of the results shows that the proposed algorithm offers superior convergence characteristics as well as time complexity as compared to the other state-of-the-art algorithms. Comparative results of normalized sum utility depict that the proposed algorithm outperforms the grey wolf optimizer (GWO), salp swarm algorithm (SSA), differential evolution (DE), whale optimization algorithm (WOA), and particle swarm optimization (PSO) based hybrid algorithms, respectively, by 0.5%, 4.3%, 6.5%, 8.6%, and 11.8%.