Abstract
Energy Efficient Resource Allocation for 5G Heterogeneous Networks Using Genetic Algorithm
Highlights
I N the rapidly evolving field of communication accompanied with continuous advancements of science and technology, there is a consensus that the number of devices connected to the wireless communication system as well as the communication traffic of entire wireless system will continue to increase exponentially
In order to observe the relationship between energy efficiency and sum throughput against different values of crossover rate, we set the number of populations to 100, the number of iterations to 30, and the variation rate to 0.001
The performance of the proposed genetic algorithm (GA) GA based scheme is compared with the existing benchmark scheme [27], which uses Dinkelbach and branch-and-bound methods to tackle the formulated mixed-integer nonlinear fractional programming (FP) problem
Summary
I N the rapidly evolving field of communication accompanied with continuous advancements of science and technology, there is a consensus that the number of devices connected to the wireless communication system as well as the communication traffic of entire wireless system will continue to increase exponentially. By decomposing the original non-convex nonlinear programming problem into two convex sub-problems, an iterative resource allocation method, and a suboptimal low-complexity algorithm were developed. The non-convex FP problem was initially transformed into two sub-problems and an iterative method and a closed-form method were proposed to solve power distribution and bandwidth allocation, respectively. The commonly proposed schemes that rely on transforming the original problem into convex form and solving it result in high computational complexity. The EE maximization problem for the downlink OFDMA HetNets is formulated It is an FP and mixedinteger nonlinear programming that cannot be solved directly. 1) In contrast to existing methods, we propose a two-step GA based resource allocation scheme to solve the EE maximization problem.
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