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Utilization of LTE-a uplink resource for cognitive radio network via matching and quantizing

机译:LTE-通过匹配和量化用于认知无线电网络的上行链路资源

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With the development of next generation mobile communications, the underlay coexistence problem of the OFD-MA based Secondary System(SS) with LTE-A systems becomes more and more important, which yet has not been studied in a systematic way. In contrast to other Primary Systems(PS), the LTE-A system puts high demands on the low complexity of the coexistence strategies. This paper focuses on the resource allocation and interference mitigation issues in the aforementioned scenario, whose objective is to protect the spectrum utilization priority of PS as well as utilize secondary resource efficiently. The difficulty lies in the fact that even the subproblem, or power allocation with interference, is NP-Hard. Therefore, this paper will propose a two-phase resource allocation algorithm using maximum weighted Matching in the subcarrier allocation phase and interference Quantizing in the power allocation phase, referred to as the MQ algorithm. As presented in this paper, the MQ algorithm enjoys the advantage of polynomial complexity of O(KJ3 + LKJ), where K, J and L denote the number of SSs, subcarriers and quantizing steps, respectively. The simulation results will show that the proposed MQ algorithm is capable of achieving near optimal system and user throughputs, which are close to the exhaustive searching algorithm.
机译:随着下一代移动通信的发展,基于OFD-MA的LTE-A系统的二次系统(SS)的底层共存问题变得越来越重要,尚未进行系统的研究。与其他主要系统(PS)相比,LTE-A系统对共存策略的低复杂性提出了很高的要求。本文针对上述场景中的资源分配和干扰缓解问题,其目的是保护PS的频谱利用优先级以及有效利用辅助资源。困难在于,即使子问题或带有干扰的功率分配也是NP-Hard。因此,本文将提出一种在子载波分配阶段使用最大加权匹配并在功率分配阶段使用干扰量化的两阶段资源分配算法,称为MQ算法。如本文所述,MQ算法具有O(KJ3 + LKJ)多项式复杂度的优势,其中K,J和L分别表示SS数,子载波和量化步长。仿真结果表明,所提出的MQ算法能够实现接近最优的系统和用户吞吐量,这与穷举搜索算法相近。

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