Browsing by Author "Chiu, Yin"

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GGRA: A Feasible Resource Allocation Scheme by Optimization Technique for IEEE 802.16 Uplink Systems
(2010-01-01)
Chang, Chung-Ju
;
Chiu, Yin
;
Feng, Kai-Ten
;
Ren, Fang-Ching
;
電信工程研究所
Generally, optimization techniques for resource allocation of OFDMA systems are infeasible for real-time applications. In this paper, we propose a genetic algorithm with subscriber station (SS) grouping resource allocation (GGRA) scheme for IEEE 802.16 uplink systems. The GGRA scheme firstly designs a rate assignment strategy, applied with a predefined residual lifetime, to allocate resource to each service dynamically. It then aggregates high correlation SSs into the same group, where the SSs will be allocated to different slots so as to avoid mutual user interference. Finally, the GGRA scheme finds an optimal assignment matrix for the system by the genetic algorithm, based on the SS groups to greatly lessen the computation complexity. The GGRA scheme can also maximize system throughput and fulfill QoS requirements. Simulation results show that the proposed GGRA scheme performs better than the EFS algorithm [6] and the MLWDF algorithm [7] in system throughput, voice/video packet drop rate, unsatisfied ratio of HTTP users/packets, and FTP throughput. The computation complexity of the GGRA scheme is also tractable and thus feasible for real-time applications.
GGRA: A Feasible Resource-Allocation Scheme by Optimization Technique for IEEE 802.16 Uplink Systems
(2010-03-01)
Chiu, Yin
;
Chang, Chung-Ju
;
Feng, Kai-Ten
;
Ren, Fang-Ching
;
電機工程學系
Generally, optimization techniques for resource allocation of orthogonal frequency-division multiple-access (OFDMA) systems are infeasible for real-time applications. In this paper, with consideration of grouping for subscriber stations (SSs), a resource-allocation scheme by an optimization technique of a genetic algorithm is proposed for the uplinks of IEEE 802.16 OFDMA systems. The genetic algorithm with SS grouping resource-allocation (GGRA) scheme first designs a rate assignment strategy that is applied with a predefined residual lifetime to dynamically allocate resource to each service. It then aggregates high-correlation SSs into the same group, where the SSs will be allocated to different slots to avoid mutual user interference. Finally, the GGRA scheme finds an optimal assignment matrix for the system by the genetic algorithm based on the SS groups to greatly lessen the computation complexity. The GGRA scheme can also maximize the system throughput and fulfill the quality-of-service (QoS) requirements. Simulation results show that the proposed GGRA scheme performs better than the efficient and fair scheduling (EFS) algorithm and the maximum largest weighted delay first (MLWDF) algorithm in system throughputs, voice/video packet drop rates, unsatisfied ratios of hypertext transfer protocol (HTTP) users/packets, and file transfer protocol (FTP) throughputs. The computation complexity of the GGRA scheme is also tractable and, thus, feasible for real-time applications.