Geo-Location Database Techniques for Incumbent Protection in the TV White Space

Gurney, David; Buchwald, Gretel; Ecklund, L.; Kuffner, S.L.; Grosspietsch, J.

doi:10.1109/dyspan.2008.31

Cited by 204 publications

(112 citation statements)

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“…In certain scenarios, more primary knowledge may be obtainable through white space database or other auxiliary means, e.g., the TV white space database [63], Geo-location [62,63], or statistical modeling of white space [61]. The design of a sequential detector with partial knowledge of the primary signal can be facilitated through conditioning the above formulations on the available information or through Bayes rule.…”

Section: Summary and Discussionmentioning

confidence: 99%

Sequential Likelihood Ratio Test under Incomplete Signal Model for Spectrum Sensing

Chung

2013

IEEE Trans. Wireless Commun.

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Abstract-Detecting the existence of the transmitter emitting signals is an important mechanism in many applications, e.g., the spectrum sensing in the cognitive radio. In conventional detection schemes, the predefined number of samples is taken for detection and the statistics of the signals are assumed to be available in the signal model. However, under the ubiquitous fading effects and the non-cooperation of the targets, the signal statistics are not accurately obtainable at the detector. In this paper, we propose a sequential detector operating on the signal model described by the autoregressive moving average (ARMA) process without assuming known coefficients. The sequential detector for the ARMA model is derived by using the likelihood ratio test framework and the predictive distributions of the ARMA process. The novelties the proposed sequential detector include: 1) performing detection without requiring complete knowledge of the signal; 2) using smaller number of samples to reach the decision on average; and 3) allowing user-specified probabilities of detection and false alarm. We derive the approximate average number of samples required to reach the decision. The energy detector and sequential energy detector are compared with the proposed sequential detector by simulations. The results show the sequential detector uses the smaller average number of samples than the energy detector and sequential energy detector to termination.

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Section: Summary and Discussionmentioning

confidence: 99%

Sequential Likelihood Ratio Test under Incomplete Signal Model for Spectrum Sensing

Chung

2013

IEEE Trans. Wireless Commun.

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“…FCC proposed a new device for a geo-location database to assist secondary users' queries and download a coverage map of information for operation on the TV white space spectrum, which includes the spectrum bands, exact types of licensed primary services present and their specific protection requirements. A set of rules is defined in the geo-location database in [48,49] to determine whether a TV channel is available for secondary users at a particular geo-graphical location, and a maximum allowed power level (per channel) at the operating location is given to avoid causing interference to primary users.…”

Section: Network Information Collectionmentioning

confidence: 99%

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

et al. 2012

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Facing the challenges on how to improve spectrum efficiency and how to realize heterogeneous network convergence in future wireless networks, a cognitive radio network (CRN) is proposed as one of the solutions. This has become a major research topic in recent years and it is timely to give an overview of the development of CRN and to summarize key issues and technologies. The fundamental concepts of CRN, including the cognitive cycle model, the network architecture, and the cognitive ability and intelligent decision functions, are introduced in detail based on recent advances. Key issues for each topic, followed with recent research on theory and method, are then classified and the industrialization developments of CRN testbeds based on TD-LTE cellular system and standards are briefly presented. Finally, conclusions are reached on the perspectives and directions of future development.cognitive radio networks, network architecture, cognitive ability, spectrum sensing, intelligent decision and dynamic spectrum allocation, end-to-end performance Driven by the requirements of ubiquitous wireless access and personalized wireless applications [1], worldwide, numerous wireless technologies are invading our lives, such as evolutionary telecommunication technologies, wideband wireless access and short distance wireless technologies. Furthermore, the concept of the internet of things (IoT) has become widespread based on the development of wireless sensor networks. With the explosive development of wireless technologies, several challenges must be addressed. The electromagnetic spectrum, whose use is licensed and managed by government, has been scarce for allocation to ubiquitous wireless applications. However, contrary to the physical scarcity of the available spectrum, the report published by the federal communication commission (FCC) shows that over 60 % of the licensed spectrum below 6 GHz remains underused [2,3]. The measurement results in Figure 1 taken in Beijing over a one month period, also indicate low spectrum use consistent with the results released by the FCC. Therefore, improvement of spectrum efficiency is the first challenge to be examined. On the other hand, the end-to-end performance of wireless networks is considered to be the performance indicator for ubiquitous and personalized wireless applications [4], such as end-to-end throughput, end-to-end delay and routing scheme, which take into account all network elements in a data transmission flow across different layers for the global optimization from the network aspect. Because the property of heterogeneity causes low radio resource use and mutual interference among heterogeneous networks, the other big challenge is that heterogeneous network convergence is needed to improve the end-to-end performance of wireless networks.Cognitive radio (CR), proposed by Mitola in 1999 [5,6], is considered a feasible solution for improving spectrum efficiency. Furthermore, a cognitive radio network (CRN), which is defined as a wireless network with the capabilities

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“…Television (TV)bands are the most attractive frequency ranges for such opportunistic use of the spectrum by SUs. This because the TV bands show high availability of WSs and their schedule use by the PUs can be obtained through Geolocation Database (GDB) services [1]. Moreover, the TV spectrum is located below 1 GHz.…”

Section: Introductionmentioning

confidence: 99%