论文标题

MMWave-Noma通过多个访问点进行交流的资源分配,考虑到人类障碍

Resource Allocation for mmWave-NOMA Communication through Multiple Access Points Considering Human Blockages

论文作者

Barghikar, Foad, Tabataba, Foroogh S., Soorki, Mehdi Naderi

论文摘要

在本文中,提出了一个新的框架,用于优化拥挤场所中的毫米波 - 非正交多访问(mmwave-noma)通信的新框架。 MMWave通讯遭受了由人体等障碍物造成的严重阻塞,尤其是在密集的地区。因此,引入了一种详细的方法,用于建模障碍事件中的障碍事件。此外,在不同的位置考虑了几个MMWave访问点。为了最大化网络总和速率,资源分配问题被提出为混合整数非线性编程,该编程通常是NP-HARD。因此,提出了三阶段的低复合解决方案来解决该问题。首先,提出了一个用户调度算法,即修改后的最差连接交换(MWCS)。其次,使用模拟退火算法解决了天线分配问题。之后,为了最大化网络和保证服务限制的质量,通过采用凸编程方法的差异来解决非凸功率分配优化问题。仿真结果表明,在阻塞效应下,提出的MMWave-Noma方案平均表现出比常规的MMWave-ottrolotal多访问方案好23%。此外,提议的解决方案的性能比最佳值低11.4%,同时将复杂性降低了96%。

In this paper, a new framework for optimizing the resource allocation in a millimeter-wave-non-orthogonal multiple access (mmWave-NOMA) communication for crowded venues is proposed. MmWave communications suffer from severe blockage caused by obstacles such as the human body, especially in a dense region. Thus, a detailed method for modeling the blockage events in the in-venue scenarios is introduced. Also, several mmWave access points are considered in different locations. To maximize the network sum rate, the resource allocation problem is formulated as a mixed integer non-linear programming, which is NP-hard in general. Hence, a three-stage low-complex solution is proposed to solve the problem. At first, a user scheduling algorithm, i.e., modified worst connection swapping (MWCS), is proposed. Secondly, the antenna allocation problem is solved using the simulated annealing algorithm. Afterward, to maximize the network sum rate and guarantee the quality of service constraints, a non-convex power allocation optimization problem is solved by adopting the difference of convex programming approach. The simulation results show that, under the blockage effect, the proposed mmWave-NOMA scheme performs on average 23% better than the conventional mmWave-orthogonal multiple access scheme. Moreover, the performance of proposed solution is 11.4% lower than the optimal value while reducing complexity by 96%.

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