Cross-layer Packet-dependant OFDM Scheduling Based on Proportional Fairness

Document Type : Research Article

Authors

1 Corresponding Author Hua Hou is with school of Information Science and Electrical Engineering, Hebei University of Engineering, Handan , P.R.China ( Email: hh110040@gmail.com).

2 Gen-xuan Li is with School of Information Science and Electrical Engineering, Hebei University of Engineering, Handan , P.R.China ( Email: hh110040@gmail.com ).

Abstract

This paper assumes each user has more than one queue, derives a new packet-dependant proportional fairness power allocation pattern based on the sum of weight capacity and the packet’s priority in users’ queues, and proposes 4 new cross-layer packet-dependant OFDM scheduling schemes based on proportional fairness for heterogeneous classes of traffic. Scenario 1, scenario 2 and scenario 3 lead respectively artificial fish swarm algorithm, self-adaptive particle swarm optimization algorithm and cloud adaptive particle swarm optimization algorithm into sub-carrier allocation in packet-dependant proportional fairness scheduling, and use respectively new power allocation pattern, self-adaptive particle swarm optimization algorithm and population migration algorithm to allocate power. Scenario 4 uses greedy algorithm concerning fairness to allocate sub-carriers, and uses new power allocation pattern to allocate power. Simulation indicates scenario 1,scenario 2 and scenario 3 raise the system’s total rate on the basis of undertaking the fairness among users’ rates and average packet delay; scenario 4 not only meets users’ rates and average packet delay demands, but also improve the fairness among users’ rates.

Keywords

References

[1]     Y.L. Liu, M.Y. Jiang.Adaptive resource allocation in multiuser OFDM system based on hopfield neural networks. JOURNAL OF CIRCUITS AND SYSTEMS,2010,15(2):47-51.
[2]     D.X. Yu, Y.M. Cai,D. Wu,W. Zhong.Subcarrier and Power Allocation Based on Game Theory in Uplink OFDMA Systems. Journal of Electronics and Information Technology,2010,32(4):775-779.
[3]     L. Peng, M.Y. Jiang. Adaptive cross-layer resource allocation scheme resisting delay sensibility. Application Research of Computers, 2010,27(3):1122-1125.
[4]     N.Zhou, X.zhu, Y.Huang, H.Lin. Low Complexity Cross-Layer Design with Packet Dependent Scheduling for Heterogeneous Traffic in Multiuser OFDM Systems. Wireless Com.,IEEE,Jun.2010,9(6):1912– 1923.
[5]     Z.K.Shen, J.G. Andrews, B.L. Evans. Adaptive Resource Allocation in Multiuser OFDM Systems With Proportional Rate Constraints. IEEE  TRANSACTIONS ON  WIRELESS COMMUNICATIONS, NOVEMBER 2005,4(6): 2726-2737.
[6]     X. Ma,Q. Liu. Artificial fish swarm algorithm for multiple knapsack problem. Journal of Computer Applications, 2010,30(2):469-471.
[7]     Y.M. Cheng, M.Y. Jiang. Adaptive resource allocation in multiuser OFDM system based on improved artificial fish swarm algorithm. Application Research of Computers, 2009,26(6):2092-2094.
[8]     X.J. Bi, W.W. Cao. Adaptive sub-carrier allocation for an orthogonal frequency division multiple access system based on a particle swarm optimization algorithm. Journal of Harbin Engineering University,2010,32(4):775-779.
[9]     K. Niu, W.W. Sun, W.J. Xu, Z.Q. He.The Distributed Power Allocation used in OFDMA systems Based on Particle Swarm Optimization Algorithm. China,201010033918.8,2010.
[10]   J. Li, Ch. Wang. A modified self-adaptive particle swarm optimization.Journal of Huazhong University of Science and Technology(Natural Science Edition),2008,36(3):118-121.
[11]   X.Q. Wei,Y.Q. Zhou,H.J. Huang,D.X. Luo. Adaptive particle swarm optimization algorithm based on cloud theory.Computer Engineering and Application, 2009,45(1):48-50.
[12]   A.J.OuYang, W.W. Zhang, Y.Q. Zhou. Hybrid global optimization algorithm based on simplex and population migration.Computer Engineering and Applications, 2010,46(4):29-31. 
 
 

Files

Share

How to cite

Statistics