無線多躍式存取是近年相當受到重視的一種傳輸技術，並且被使用在最新的IEEE 802.16j 多躍中繼網路上，相較於傳統單躍式網路，多躍中繼網路能夠大幅改善使用者的無線傳輸品質並且提升系統整體效能。為了達到最高的頻寬資源使用率，現今絕大多數的資源排程方法皆是基於動態資源分配原則所設計，雖然動態分配能夠達到良好的資源使用率，卻也大幅提升了系統運算的負擔。由於在實際的IEEE 802.16j 多躍中繼網路中，排程時間非常短，因此，這類型基於動態資源分配的排程演算法可能並不適合套用在實際的系統環境中，為了降低系統運算的負擔並且讓系統整體效能維持在一定的水準，我們針對IEEE 802.16j 多躍中繼網路提出了一個以頻寬重整與借用為基礎之快速排程演算法，藉由將頻寬重整與借用之演算法的部分運算負擔分散在系統排程時間之外，再結合靜態資源分配的優點，經由豐富的實驗數據驗證了：不管是在對稱式或非者對稱式的網路架構中，我們提出的資源排程演算法能在常數時間內達到接近最佳資源使用率的成效。

Multihop relay (MR) technology is one of the most promising technologies that provide performance enhancement to the existing network systems. To achieve the maximal system performance, most current research allocates resources in a dynamic manner. However, in MR networks, the issues of path selection and spatial reuse will massively increase the complexity of resource scheduling. As a result, the majority of dynamic scheduling schemes might be not applicable in practical implementation due to high computation complexity. In this paper, to reduce the scheduling complexity while achieving near-optimal system throughput, we propose a constant-time repacking and borrowing-based resource scheduling (RBRS) algorithm for IEEE 802.16j MR networks. In the repacking phase of RBRS, the connection which served with high-cost path can be handed off to low-cost path and thus increase the number of available resources. In the borrowing phase, the overloaded cells are capable of borrowing resources from the under-loaded cells and thus the resource utilization can be further improved. Since both the repacking and borrowing processes are executed only with computation overhead of the selection operation, RBRS can make each scheduling decision in constant time. Simulation models are developed to investigate the performance of RBRS. The simulation results indicate that RBRS is at least 8.91 times faster than the state-of-the-art dynamic scheduling scheme while yielding near-optimal throughput. Even in the asymmetric environments, RBRS strikes an excellent balance between the system throughput and scheduling computation time.

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