Abstract
The cell assignment problem is combinatorial, with increased complexity when it is tackled considering resource allocation. This paper models joint cell assignment and resource allocation for cellular heterogeneous networks, and formalizes cell assignment as an optimization problem. Exact algorithms can find optimal solutions to the cell assignment problem, but their execution time increases drastically with realistic network deployments. In turn, heuristics are able to find solutions in reasonable execution times, but they get usually stuck in local optima, thus failing to find optimal solutions. Metaheuristic approaches have been successful in finding solutions closer to the optimum one to combinatorial problems for large instances. In this paper we propose a fast and efficient heuristic that yields very competitive cell assignment solutions compared to those obtained with three of the most widely-used metaheuristics, which are known to find solutions close to the optimum due to the nature of their search space exploration. Our heuristic approach adds energy expenditure reduction in its algorithmic design. Through simulation and formal statistical analysis, the proposed scheme has been proved to produce efficient assignments in terms of the number of served users, resource allocation and energy savings, while being an order of magnitude faster than metaheuritsic-based approaches.
Highlights
Current forecasts indicate that wireless traffic demand will continuously increase in the near future, reaching more than 4.5 ZB in the years [8]
It is worth mentioning that heuristic-based approaches are able to find solutions to practical optimization problems with reasonable execution times, but they usually get stuck in local optima, failing to find better solutions, and metaheuristic approaches have been successful in finding closer solutions to the optimum to combinatorial problems [5], such as the one we tackle in this work
This Section describes the metaheuristics that have been compared wiith our proposed cell assignment approach, we describe basic concepts of metaheuristic- namely Harmony Search (HS), Simulated annealing (SA), based optimization and the solutions that have been and Genetic algorithm (GA)
Summary
Current forecasts indicate that wireless traffic demand will continuously increase in the near future, reaching more than 4.5 ZB in the years [8]. The most noteworthy contribution is that we use a novel optimization technique, which is able to yield similar results to other metaheuristic-based solutions, but is an order of magnitude faster We exploit it to solve a joint cellular assignment/resource allocation problem, which has been tackled using different approaches. We analyzed the following parameters: joint access selection and resource allocation; heterogeneous networks scenario; whether energy efficiency is addressed; and whether the proposed solution takes into account the actual traffic demand of users, in contrast to saturation of full-buffer conditions. It is worth mentioning that heuristic-based approaches are able to find solutions to practical optimization problems with reasonable execution times, but they usually get stuck in local optima, failing to find better solutions, and metaheuristic approaches have been successful in finding closer solutions to the optimum to combinatorial problems [5], such as the one we tackle in this work
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