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全双工多跳无线网络优化中的连续干扰消除。

Full-Duplex Multi-Hop Wireless Networks Optimization with Successive Interference Cancellation.

机构信息

School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China.

出版信息

Sensors (Basel). 2018 Dec 6;18(12):4301. doi: 10.3390/s18124301.

DOI:10.3390/s18124301
PMID:30563241
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6308744/
Abstract

In wireless network communication, in-band full-duplex technique is a useful and important technique that can enlarge the whole throughput of the wireless networks. However, its use needs harsh environment. The successive interference cancellation can make several transmitters' data be received simultaneously by the receiver, and can make the in-band full-duplex technique be used easily in reality. In this paper, we try to propose an optimal algorithm for increasing the throughput of full-duplex multi-hop wireless networks with successive interference cancellation, which we call the full-duplex successive interference cancellation (FD-SIC) wireless networks. We first describe the mathematical model for the FD-SIC wireless networks and show it is NP-hard in general. Then, we propose a heuristic algorithm, namely the use-up-link-capacity iterative (UULC-iterative) algorithm, for each node's routing and transmitting scheme. Simulation results show that the proposed algorithm for FD-SIC wireless networks can achieve better throughput compared with SIC-only networks and the interference avoidance networks.

摘要

在无线网络通信中,带内全双工技术是一种有用且重要的技术,它可以提高无线网络的整体吞吐量。然而,它的使用需要苛刻的环境。连续干扰消除技术可以使接收器同时接收多个发送器的数据,并使带内全双工技术在实际中更容易使用。在本文中,我们尝试提出一种最优算法,用于提高具有连续干扰消除的全双工多跳无线网络的吞吐量,我们称之为全双工连续干扰消除(FD-SIC)无线网络。我们首先描述了 FD-SIC 无线网络的数学模型,并证明了它通常是 NP 难的。然后,我们为每个节点的路由和传输方案提出了一种启发式算法,即上行链路容量消耗迭代(UULC-iterative)算法。仿真结果表明,与仅使用 SIC 的网络和干扰避免网络相比,所提出的 FD-SIC 无线网络算法可以获得更好的吞吐量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/82dda9783611/sensors-18-04301-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/1fc90d0b00bd/sensors-18-04301-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/ca61fe9d94e1/sensors-18-04301-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/fe7e65b1305f/sensors-18-04301-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/aa9fbecd2cf8/sensors-18-04301-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/58a0409b02ad/sensors-18-04301-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/9ac2dcfb83e2/sensors-18-04301-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/945f1b9eb4b5/sensors-18-04301-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/1e56a02122d6/sensors-18-04301-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/82dda9783611/sensors-18-04301-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/1fc90d0b00bd/sensors-18-04301-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/ca61fe9d94e1/sensors-18-04301-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/fe7e65b1305f/sensors-18-04301-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/aa9fbecd2cf8/sensors-18-04301-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/58a0409b02ad/sensors-18-04301-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/9ac2dcfb83e2/sensors-18-04301-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/945f1b9eb4b5/sensors-18-04301-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/1e56a02122d6/sensors-18-04301-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ad9/6308744/82dda9783611/sensors-18-04301-g009.jpg

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本文引用的文献

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