基于分区信道筛选机制的5G网络吞吐量优化算法

发布时间：2018-05-19 16:22

  本文选题：无线移动异构网络 + 信道传输矩阵映射　； 参考：《计算机工程与设计》2017年09期

【摘要】：为解决当前5G无线移动异构网络数据吞吐算法中存在的虚拟分区间信号漂移严重,且难以实现用户终端与小区基站之间平衡切换的不足,提出基于密集虚拟分区信道筛选机制的5G无线移动异构网络吞吐量优化算法。通过5G信号密集化过程,构建密集采样机制,将离散的信号密集进行并发多路处理;采取虚拟分区映射机制,将待传输的5G信号精确划入最佳匹配的分区中,降低高密度无线信号在发射接收过程中的互相影响;构造信道传输矩阵映射机制,通过引入拉氏变换变化技术,提高信号传输矩阵中可承载的数据上传带宽容量,联合采取容量扩容机制,改善信号密集条件下5G信号可利用的上传带宽容量。仿真结果表明,与当前UPOS-1、UP5G-Plus、UPC-ad算法相比,在5G无线网络异构程度较高时,该算法拥有更高的数据吞吐量,在节点密集条件下能够有效提升数据传输效率,显著削弱了小区间同频信号的互相干扰。
[Abstract]:In order to solve the problem that the virtual sub-interval signal drift is serious in the current 5G wireless mobile heterogeneous network data throughput algorithm, it is difficult to realize the balanced handover between the user terminal and the cell base station. A 5G wireless mobile heterogeneous network throughput optimization algorithm based on dense virtual partition channel filtering mechanism is proposed. Through the process of 5G signal densification, the dense sampling mechanism is constructed, the discrete signal is processed simultaneously and multiplex, and the 5G signal to be transmitted is accurately divided into the best matching partition by using the virtual partition mapping mechanism. The mutual influence of high density wireless signal during transmission and reception is reduced, and the channel transmission matrix mapping mechanism is constructed. By introducing the Laplace transform change technique, the data upload bandwidth capacity of the signal transmission matrix can be increased. The capacity expansion mechanism is adopted to improve the available upload bandwidth of 5G signal under the condition of dense signal. The simulation results show that compared with the current UPOS-1 UP5G-PlusPad algorithm, the algorithm has higher data throughput when the heterogeneity of the 5G wireless network is high, and can effectively improve the data transmission efficiency under the condition of dense nodes. The mutual interference of the same frequency signal in the interval is significantly reduced.
【作者单位】： 安阳工学院计算机科学与信息工程学院;
【基金】：国家自然科学基金河南人才培养联合基金项目(U1204613)
【分类号】：TN929.5
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本文编号：1910780