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Leveraging Multi-User Diversity, Channel Diversity and Spatial Reuse for Efficient Scheduling in Wireless Relay Networks

机译:利用多用户分集,信道分集和空间重用在无线中继网络中进行高效调度

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Relay stations can be deployed in a wireless network to extend its coverage and improve its capacity. In this paper, we study a scheduling problem in OFDMA-based wireless relay networks with consideration for multi-user diversity, channel diversity and spatial reuse. First, we present a Mixed Integer Linear Programming (MILP) formulation to provide optimum solutions. It has been shown by previous research that performance of a wireless scheduling algorithm is usually related to the interference degree delta, which is the maximum number of links that interfere with a common link but do not interfere with each other. Therefore, we then show that the interference degree delta is at most 4 for any 2-hop relay network and 14 for any general h-hop (h > 1) relay network. Furthermore, we present a simple greedy algorithm for the scheduling problem and show it has an approximation ratio of 1/(1+delta), which leads to an approximation ratio of 1/5 for the 2-hop case and 1/15} for the general case. In addition, we present three heuristic algorithms, namely, the weighted degree greedy algorithm, the Maximum Weighted Independent Set (MWIS) algorithm and the Linear Programming (LP) rounding algorithm, to solve the scheduling problem. Extensive simulation results have showed that the LP rounding algorithm performs best and always provides close-to-optimum solutions. The performance of the simple greedy algorithm is comparable to that of the other algorithms.
机译:中继站可以部署在无线网络中,以扩展其覆盖范围并提高其容量。在本文中,我们研究了基于OFDMA的无线中继网络中的调度问题,其中考虑了多用户分集,信道分集和空间重用。首先,我们提出一种混合整数线性规划(MILP)公式,以提供最佳解决方案。先前的研究表明,无线调度算法的性能通常与干扰程度增量有关,干扰程度增量是干扰公共链路但互不干扰的最大链路数。因此,我们然后表明,对于任何2跳中继网络,干扰度增量最多为4,而对于任何常规h-hop(h> 1)中继网络,干扰度增量最多为14。此外,我们针对调度问题提出了一种简单的贪心算法,并显示了其近似比率为1 /(1 + delta),这导致2跳情况的近似比率为1/5,而2跳跃情况的近似比率为1/15}。一般情况。此外,我们提出了三种启发式算法,即加权度贪婪算法,最大加权独立集(MWIS)算法和线性规划(LP)舍入算法,以解决调度问题。大量的仿真结果表明,LP舍入算法性能最佳,并且始终提供接近最佳的解决方案。简单贪婪算法的性能可与其他算法媲美。

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