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通过虚拟化和机会信道分配最大化无线网络的资源效率。

Maximizing Resource Efficiency in Wireless Networks through Virtualization and Opportunistic Channel Allocation.

机构信息

Department of Master's Degree in ICT for Education, Smart Grid Research Group (GIREI), Universidad Politécnica Salesiana, Quito EC170525, Ecuador.

Telecommunications Research Group (GITEL), Universidad Politécnica Salesiana, Cuenca EC010102, Ecuador.

出版信息

Sensors (Basel). 2023 Apr 13;23(8):3949. doi: 10.3390/s23083949.

DOI:10.3390/s23083949
PMID:37112290
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10143220/
Abstract

Wireless cellular networks have become increasingly important in providing data access to cellular users via a grid of cells. Many applications are considered to read data from smart meters for potable water, gas, or electricity. This paper proposes a novel algorithm to assign paired channels for intelligent metering through wireless connectivity, which is particularly relevant due to the commercial advantages that a virtual operator currently provides. The algorithm considers the behavior of secondary spectrum channels assigned to smart metering in a cellular network. It explores spectrum reuse in a virtual mobile operator to optimize dynamic channel assignment. The proposed algorithm exploits the white holes in the cognitive radio spectrum and considers the coexistence of different uplink channels, resulting in improved efficiency and reliability for smart metering. The work also defines the average user transmission throughput and total smart meter cell throughput as metrics to measure performance, providing insights into the effects of the chosen values on the overall performance of the proposed algorithm.

摘要

无线蜂窝网络通过网格单元为蜂窝用户提供数据访问变得越来越重要。许多应用程序被认为可以读取智能电表中的饮用水、天然气或电力数据。本文提出了一种通过无线连接为智能计量分配配对信道的新算法,由于虚拟运营商目前提供的商业优势,这一算法尤其具有现实意义。该算法考虑了分配给蜂窝网络中智能计量的辅助频谱信道的行为。它探索了虚拟移动运营商中的频谱复用,以优化动态信道分配。所提出的算法利用认知无线电频谱中的白洞,并考虑不同上行链路信道的共存,从而提高智能计量的效率和可靠性。该工作还定义了平均用户传输吞吐量和总智能计量小区吞吐量作为衡量性能的指标,深入了解所选值对所提出算法整体性能的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/0694119a3271/sensors-23-03949-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/45ce0ad422f1/sensors-23-03949-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/1a0dc2116c87/sensors-23-03949-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/b9137078dfed/sensors-23-03949-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/a65913a81cc1/sensors-23-03949-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/50201b8e21d4/sensors-23-03949-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/51a1b2b0a50d/sensors-23-03949-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/0694119a3271/sensors-23-03949-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/45ce0ad422f1/sensors-23-03949-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/1a0dc2116c87/sensors-23-03949-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/b9137078dfed/sensors-23-03949-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/a65913a81cc1/sensors-23-03949-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/50201b8e21d4/sensors-23-03949-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/51a1b2b0a50d/sensors-23-03949-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a3/10143220/0694119a3271/sensors-23-03949-g007.jpg

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