Distributed Spatio-Temporal Spectrum Sensing: An Experimental Study

Raman, Chandrasekharan; Kalyanam, Janani; Seskar, Ivan; Mandayam, Narayan B.

doi:10.1109/acssc.2007.4487600

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…3) Available-spectrum: The spectrum space not consumed by the transmitters and receivers is the available-spectrum within a geographical region. Figure 6 depicts spatial distribution of unit-spectrum-space opportunity given by (19) for the above topology. The available-spectrum space is 563.6 W m 2 (83.4% of the total spectrum space).…”

Section: ) Spectrum Consumed By a Transmittermentioning

confidence: 99%

“…Traditionally the performance of spectrum recovery is measured in terms of the throughput for the secondary users and outage probability [15], [16], [17]. The performance of detection of spectrum holes is also captured in terms of probability of missed detection and false positives [18], [19], [20]. However, this characterization of the performance is in the context of spectrum sharing constraints defined by a certain spectrum sharing model or in terms of system-level ob-jectives.…”

Section: Al Classified Spectrum Sharing Approaches Into Open Sharing ...mentioning

confidence: 99%

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MUSE: A Methodology for Quantifying Spectrum Usage

Khambekar

Spooner

Chaudhary

2016

2016 IEEE Global Communications Conference (GLOBECOM)

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Dynamic spectrum sharing paradigm is envisaged to meet the growing demand for the Radio Frequency (RF) spectrum. There exist several technical, regulatory, and business impediments for adopting the new paradigm. In this regard, we underscore the need of characterizing and quantifying the use of spectrum by each of the individual transmitters and receivers.We propose MUSE, a methodology to characterize and quantify the use of spectrum in the space, time, and frequency dimensions. MUSE characterizes the use of spectrum by a transmitter at a point in terms of the RF power occupied by the transmitter. It characterizes the use of spectrum by a receiver at a point in terms of the constraints on the RF-power that can be occupied by any of the transmitters in the system in order to ensure successful reception. It divides the spectrum-space into discrete unit-spectrumspaces and quantifies the spectrum used by the individual transceivers in the discretized spectrum space.We characterize the performance of the spectrum management functions in the discretized spectrumspace and illustrate maximizing the use of spectrum. In order to address the challenges for the dynamic spectrum sharing paradigm, we emphasize on articulating, defining, and enforcing the spectrum-access rights in the discretized spectrum-space.

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Section: ) Spectrum Consumed By a Transmittermentioning

confidence: 99%

Section: Al Classified Spectrum Sharing Approaches Into Open Sharing ...mentioning

confidence: 99%

MUSE: A Methodology for Quantifying Spectrum Usage

Khambekar

Spooner

Chaudhary

2016

2016 IEEE Global Communications Conference (GLOBECOM)

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“…in [3], Dogandzic performed a single PU localization in wireless fading channels based on EM algorithm. In [4], C.raman Introduced an experimental study to estimate the performance of localization. Those algorithms are efficient for localizing single primary user in the network, but as two or more PUs exist, they can not work well.…”

Section: Introductionmentioning

confidence: 99%

An iterative multiple primary users localization algorithm based on clustering

Xing

Han

2011

2011 6th International ICST Conference on Communications and Networking in China (CHINACOM)

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Multiple co-channel primary users (PU) existing simultaneously is very common in cognitive radio environments. With the knowledge of the location information of PUs, the cognitive radio can use spectrum-access opportunities more effectively and provides more value-added services. A novel multiple PUs localization algorithm was proposed, which seeks non-cooperative PUs' position by executing k-mean clustering and iterative operations. Simulation results show that the proposed method achieves better performance than traditional expectation-maximization(EM) algorithm.

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“…In [11], the particle swarm optimization technique is used, whereas in [12] the problem is solved by applying the expectationmaximization algorithm. An experimental study, employing a triangulation-based heuristic approach for multiple transmitter localization using synchronized sensing is presented in [13]. Range-free localization of the primary is proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

Measurement clustering criteria for localization of multiple transmitters

Nasif

Mark

2009

2009 43rd Annual Conference on Information Sciences and Systems

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Abstract-We consider the problem of localizing multiple cochannel transmitters belonging to a licensed or primary network using signal strength measurements taken by a group of unlicensed or secondary nodes. Traditional localization techniques can be applied to multiple transmitter localization, provided that: (1) the total number of cochannel transmitters in the system is known, and (2) an appropriate set of clustered measurements is available. In this paper, we present two criteria to determine the total number of cochannel transmitters in the primary system. The first criterion is called the net MMSE criterion, which uses the Cramér-Rao lower bound on localization accuracy. The second criterion is the information theoretic criterion, minimum description length. Both of these criteria lead to measurement clustering algorithms in a natural way. Although we consider only signal strength measurements, the approach can be generalized to include other types of observations (e.g., time and angle information) with independent measurements in additive noise. Our numerical results demonstrate the effectiveness of the proposed approach to measurement clustering.

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Distributed Spatio-Temporal Spectrum Sensing: An Experimental Study

Cited by 14 publications

References 4 publications

MUSE: A Methodology for Quantifying Spectrum Usage

MUSE: A Methodology for Quantifying Spectrum Usage

An iterative multiple primary users localization algorithm based on clustering

Measurement clustering criteria for localization of multiple transmitters

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