A Novel Genetic Resource Allocation Algorithm for Symmetrical Services in OFDMA Systems
Swapna P. S.1, Sakuntala S. Pillai2, Syama Sasi Kumar3, Sreeni K. G4
1Swapna P.S, Department of ECE, Mar Baselios College of Engineering and Technology, Trivandrum ( Kerala), India.
2Sakuntala S. Pillai, Department of ECE, Mar Baselios College of Engineering and Technology, Trivandrum ( Kerala), India.
3Syama Sasi Kumar, Department of ECE, Mar Baselios College of Engineering and Technology, Trivandrum ( Kerala), India.
4Sreeni K.G, Department of ECE, College of Engineering, Trivandrum ( Kerala), India.
Manuscript received on 14 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 40-44 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10091291S319/19©BEIESP | DOI: 10.35940/ijeat.A1009.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Performance enhancement of symmetrical services has been very essential today owing to the widespread acceptance and demand of these services in the present generation communication systems. An algorithm with reduced complexity for subcarrier allocation in OFDMA/SC-FDMA system for specific applications that demand similar bidirectional quality is proposed in this paper. The resource allocation problem devised is a multiobjective optimization problem with objectives to maximize bidirectional data rates and minimize the difference in bidirectional data rates, with fairness as a significant constraint. The original problem is mathematically intractable due to non-convexity and therefore linear programming techniques fail to find an optimal solution. The subcarrier allocation problem has been undertaken using an innovative multiobjective optimization technique that employs the concept of non-dominance in evolutionary algorithms. The results are extremely encouraging, while significantly reducing the complexity involved in the processing of algorithm.
Keywords: Evolutionary Algorithm, Multi Objective Optimization, Non-dominance, OFDMA/SC-FDMA.
Scope of the Article: Cloud Resources Utilization in IoT