Resource Allocation Strategy for D2D assisted Edge Computing System Using Optimization Algorithms

The constraints owing to the usage of batteries as well as cellular clients' restrictions in computing capability cause concern. This is related to allocating resources in Device to Device (D2D) aided edge computing systems. Hence, these prompt the respective approaches to investigate the harnessing of electrical energy in a hybrid way. The computing resource of the Mobile Edge Computing (MEC) server reaches the limit of its computing capability. The adjacent base station's user can act as a relay node by setting up the D2D relay links for the computing responsibilities of the users. Earlier these were left under the previous base station. Now they can be transferred to the new base station's MEC server. This has enough resources. The goal of the resource allocation approaches is to improvise energy efficiency under computation delay constraints and energy harvesting constraints. The formulation of the problem in the proposal is done as a mixed-integer non-linear problem (MINLP). It achieves one of the best solutions. The computational intricacy is small. Hence, the paper proposes a Weighted Genetic Algorithm (WGA) pivoted resource allocating method for 5G networks' D2D type communications. Genetic algorithms assumed due popularity for allocating networks as well as rendering them optimized. An optimal effect is realized by adopting the optimization algorithm like WGA rather than the algorithms namely Ant Colony Optimization (ACO), Standard Particle Swarm Optimization (SPSO) and Quantum Behaved Particle Swarm Optimization (QPSO). Simulated outcomes infer that WGA has an edge over the mentioned algorithms in energy efficiency, SINR and throughput. Added to that studies imply that such schemes' self-learning ability (i.e. WGA, ACO) yields improved results for problems with higher complexity.