Particle Swarm Optimization Based Approach for Resource Allocation and Scheduling in OFDMA Systems

Chilukuri Kalyana Chakravarthy, Prasad Reddy
DOI: 10.4236/ijcns.2010.35062                

Abstract

Orthogonal Frequency-Division Multiple Access (OFDMA) systems have attracted considerable attention through technologies such as 3GPP Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). OFDMA is a flexible multiple-access technique that can accommodate many users with widely varying applications, data rates, and Quality of Service (QoS) requirements. OFDMA has the advantages of handling lower data rates and bursty traffic at a reduced power compared to single-user OFDM or its Time Division Multiple Access (TDMA) or Carrier Sense Multiple Access (CSMA) counterparts. In our work, we propose a Particle Swarm Optimization based resource allocation and scheduling scheme (PSORAS) with improved quality of service for OFDMA Systems. Simulation results indicate a clear reduction in delay compared to the Frequency Division Multiple Access (FDMA) scheme for resource allocation, at almost the same throughput and fairness. This makes our scheme absolutely suitable for handling real time traffic such real time video-on demand.

Keywords

OFDMA, Resource Allocation, Scheduling, Quality of Service, Delay

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	D. Kivanc, G. Li and H. Liu, “Computationally Efficient Bandwidth Allocation and Power Control for OFDMA,” IEEE Transactions on Wireless Communications, Vol. 2, No. 6, 2003, pp. 1150-1158.
[2]	C. Wong, R. Cheng, K. Letaief and R. Murch, “Multiuser OFDM with Adaptive Subcarrier, Bit, and Power Allocation,” IEEE Journal on Selected Areas in Communications, Vol. 17, No. 10, 1999, pp. 1747-1758.
[3]	J. Jang and K. Lee, “Transmit Power Adaptation for Multiuser OFDM Systems,” IEEE Journal on Selected Areas in Communications, Vol. 21, No. 2, 2003, pp. 171-178.
[4]	G. Li and H. Liu, “On the Optimality of the OFDMA Network,” IEEE Communications Letters, Vol. 9, No. 5, 2005, pp. 438-440.
[5]	C. Mohanram and S. Bhashyam, “A Sub-optimal Joint Subcarrier and Power Aallocation Algorithm for Multiuser OFDM,” IEEE Communications Letters, Vol. 9, No. 8, 2005, pp. 685-687.
[6]	G. Manimaran and C. Siva Ram Murthy, “A Fault-Tolerant Dynamic Scheduling Algorithm for Multiprocessor Real-Time Systems and Its Analysis,” IEEE Transactions on Parallel and Distributed Systems, Vol. 9, No. 11, 1998, pp. 1137-1152.
[7]	R. Chen, J. G. Andrews, R. W. Heath and A. Ghosh, “Uplink Power Control in Multi-Cell Spatial Multiplexing Wireless Systems,” IEEE Transactions on Wireless Communications, Vol. 6, No. 7, 2007, pp. 2700-2711.
[8]	A. J. Page and T. J. Naughton, “Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms,” 15th Artificial Intelligence and Cognitive Science Conference, Ireland, 2004, pp. 137-146.
[9]	A. J. Page and T. J. Naughton, “Dynamic Task Scheduling Using Genetic Algorithms for Heterogeneous Distributed Computing,” Proceedings of the 19th IEEE/ACM International Parallel and Distributed Processing Symposium, Denver, 2005, pp. 1530-2075.
[10]	A. S. Wu, H. Yu, S. Jin, K.-C. Lin and G. Schiavone, “An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling,” IEEE Transactions on Parallel and Distributed Systems, Vol. 15, No. 9, 2004, pp. 824-834.
[11]	A. Y. Zomaya and Y.-H. Teh, “Observations on Using Genetic Algorithms for Dynamic Load-Balancing,” IEEE Transactions on Parallel and Distributed Systems, Vol. 12, No. 9, 2001, pp. 899-911.
[12]	E. S. H. Hou, N. Ansari and H. Ren, “A Genetic Algorithm for Multiprocessor Scheduling,” IEEE Transactions on Parallel and Distributed Systems, Vol. 5, No. 2, 1994, pp. 113-120.
[13]	R. Eberhart and Y. Shi, “Particle Swarm Optimization: Developments, Applications and Resources,” IEEE International Conference on Evolutionary Computation, Seoul, 2001, pp. 81-86.
[14]	J. Kennedy, “The Particle Swarm: Social Adaptation of Knowledge,” IEEE International Conference on Evolutionary Computation, Indianapolis, 1997, pp. 303-308.
[15]	F. van den Bergh, “An Analysis of Particle Swarm Optimizers,” PhD Thesis, University of Pretoria, 2001.
[16]	J. Blondin, “Particle Swarm Optimization: A Tutorial,” September 2009.