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.%The signal drift between the virtual partitions is serious in the current 5G wireless mobile heterogeneous network data throughput algorithm,and it is difficult to realize the balance between the user terminal and the cell base station.To solve the mentioned problems,throughput optimization algorithm for 5G wireless mobile heterogeneous networks based on dense virtual partition channel filtering mechanism was proposed.By constructing a 5G signal intensive process,the discrete signal was transmitted for intensive residential mode.Virtual partition mapping method was taken,5G signals to be transmitted were accurately assigned to the virtual area with best match,reducing the influence of signal fluctuation on the transmission process.Channel transmission matrix mapping mechanism was constructed,and through the Laplace transformation way,the signal transmission matrix could carry more data upload bandwidth.Capacity enhancement mechanism was adopted to improve the upload bandwidth capacity that 5G signal could use under the condition of intensive signal.Simulation results show that compared with the traditional UPOS-1,UP5G-Plus,UPC-ad algorithm,when the heterogeneous degree is higher in 5G wireless network,the proposed algorithm has higher data throughput,and it can greatly improve the data transmission rate under the condition of intensive signal,significantly weakening the mutual interference between cells of signals with the same frequency.
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