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Cognitive Radio Network Tomography

delete2010-05-01
delete45
PRE
AI
C
Chung-Kai Yu *
K
Kwang‐Cheng Chen
S
Shin‐Ming Cheng
DOI:10.1109/TVT.2010.2044906delete
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摘要

摘要

En 中文
The cognitive radio network (CRN), as a promising technique in future wireless communication networks, shall execute some critical functionalities to enhance existing wireless networks, such as network reconfigurability to adaptively select networks (e.g., in IEEE P1900.4 and ETSI-RSS), spectrum opportunity utilization for transmissions over opportunistic links to enhance spectrum efficiency (e.g., in IEEE 802.22), and further cooperative relays among cognitive radios (CRs) and nodes of coexisting multiradio systems, including heterogeneous primary systems. To support multilink operations and networking functions in CRN, traditional spectrum sensing is not enough, and we thus develop CRN tomography to meet the general needs of CRN operations at both the link and network levels. Borrowing the concept from medical/Internet tomography via statistical inferring, we establish the framework and methodology of CRN tomography that can be passive monitoring or active probing defined over link-or network-level parameter inference. Generally speaking, conventional CR spectrum-sensing techniques belong to the category of the passive link-level monitoring. Multiple-system sensing and identification can be considered as a sort of passive network-level CRN tomography. We further propose active link-level CRN tomography by examining the radio resource for transmissions. Finally, CRN tomography using active network-level probing is illustrated by the estimation of successful packet-transmission probability in network operations. This paper initiates explorations of CRN tomography obtaining the required parameters at the link and network levels for successful CRN operations.
Keyword:
Cognitive radio networks (CRNs)
multiple-system sensing
network tomography
radio-resource sensing
spectrum sensing
successful transmission probability estimation
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期刊

IEEE Transactions on Vehicular Technology 封面图
IEEE Transactions on Vehicular Technology
IF:
7.1
论文数:
1.8W
被引数:
6.6W

机构

N
National Taiwan University
学者数:
4.7W
论文数: 4.2W
被引数: 3.6W
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