Adaptive Communication for Capacity Enhancement: A Hybrid Intelligent Approach
Adaptive/dynamic Resource allocation plays a vital role in Wireless Communication Systems (WCS). This paper deals with hybridization of the two evolutionary computing techniques namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) investigated for adaptive resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA). It involves adaptive subchannel and power allocation. OFDM is the most widely used in almost every communication network ranging from DAB, DVB, 3G–5G mobile, WiMAX and WiFi etc. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best possible gain by proposed hybrid optimization technique coined as Sequential Particle Swarm Optimization-Genetic Algorithm (SPSO-GA) and Sequential Genetic Algorithm-Particle Swarm Optimization (SGA-PSO). The intention of the work is to improve the total capacity under the total power and fairness constraints. The sum capacity is increased and the computational time is reduced, compared the existing techniques. The simulation results reveal a notable enhancement in the performance of the algorithm with increase in number of users supported. The capacity obtained by SPSO-GA is found better than existing algorithms.
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Document Type: Research Article
Publication date: April 1, 2018
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- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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