When is electromagnetic spectrum fungible?

Weiss, Martin B. H.; Krishnamurthy, P.; Doyle, Linda; Pelechrinis, Konstantinos

doi:10.1109/dyspan.2012.6478158

Cited by 15 publications

(25 citation statements)

References 18 publications

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“…If at the end of the bidding rounds, an SLR is not able to obtain bandwidth units of spectrum, it will opt for alternate technology (AT) units to satisfy its requirements. As specified in [2] the AT units can be viewed as alternate equipment the user can utilize instead of the spectrum offered by the BM for fulfilling its traffic demands 1 . In this work we have assumed that an AT unit will have the same capabilities as the preferred frequency of an SLR.…”

Section: A Model Characteristicsmentioning

confidence: 99%

“…For presenting these scores, we refine the approach used in [1]. We determine probabilistic and distance fungibility scores by comparing the results obtained with a reference frequency f 1 to those obtained with a frequency f 2 .…”

Section: Fungibility Scoresmentioning

confidence: 99%

“…The use of fungibility scores have been proposed to achieve this [1], which could quantify the level of comparability between two frequencies (e.g., the preferred and available frequency bands). The authors of that paper considered the free-space propagation loss differences across electromagnetic frequencies to determine how substitutable a certain frequency band is with another.…”

Section: Fungibility Scoresmentioning

confidence: 99%

“…That is, spectrum availability in one geographic region cannot satisfy demands in another region and the propagation of electromagnetic waves is a function of frequency, which means that one band is not necessarily fungible with another band. The limitations of spectrum fungibility was first examined in [1]. In this paper, a fungibility score was proposed as a means to establish a level of substitutability of two different frequencies.…”

Section: Introductionmentioning

confidence: 99%

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How Do Limitations in Spectrum Fungibility Impact Spectrum Trading?

Gomez

Weiss

2013

SSRN Journal

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Abstract-Secondary markets for spectrum trading have been considered an important solution for generating spectrum opportunities in an environment where scarcity is the rule. Nonetheless, an important factor when envisioning a successful spectrum trading environment is to consider how comparable an available frequency is to the frequency an spectrum user prefers. With this aim, we consider the fungibility scores previously determined in [1] in order to explore further parameters that can influence this quantification of the level of fungibility. Further, we merge these fungibility calculations with an existing spectrum trading model, SPECTRAD [2], seeking to determine the actual impact of the limitations of spectrum fungibility in the market viability.

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Section: A Model Characteristicsmentioning

confidence: 99%

Section: Fungibility Scoresmentioning

confidence: 99%

Section: Fungibility Scoresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How Do Limitations in Spectrum Fungibility Impact Spectrum Trading?

Gomez

Weiss

2013

SSRN Journal

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“…Fungibility of the aggregated spectrum is considered in [17], where the authors seek to identify whether all spectrum bands provide the same performance. For example, low frequency spectrum, such as the 600 MHz band considered in our paper, is generally viewed as more desirable than higher frequencies because of its propagation properties.…”

Section: Related Workmentioning

confidence: 99%

Network dimensioning with carrier aggregation

Kavurmacioglu

Starobinski

2015

2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

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Abstract-A recent policy ruling by the Federal Communications Commission (FCC) set aside a fixed amount of cleared spectrum for smaller network providers. Thanks to this ruling, smaller providers can improve their quality of service using carrier aggregation. In this paper, we determine the optimal (minimum) level of carrier aggregation that a smaller provider needs in order to bring its service in line with a larger provider in the same market. Toward this end, we provide an asymptotically exact formula for the loss (blocking) probability of flows under a quality-driven (QD) regime. Using this formula, we establish an efficient way of numerically calculating the optimal level of carrier aggregation and derive scaling laws. Specifically, we show that the optimal level of carrier aggregation scales sub-linearly with respect to the scaling factor, i.e., the ratio between the network capacities of the two providers, and decreases with the initial traffic load of the providers. We derive a closed-form linear upper bound on the optimal level of carrier aggregation and prove that it is the tightest possible. We provide numerical results, showing the accuracy of our methods and illustrating their use. We also discuss the extension of our results to delayrelated metrics as well as their application to profitable pricing in secondary spectrum markets.

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A comprehensive spectrum trading scheme based on market competition, reputation and buyer specific requirements

2015

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a b s t r a c tIn the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively.

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When is electromagnetic spectrum fungible?

Cited by 15 publications

References 18 publications

How Do Limitations in Spectrum Fungibility Impact Spectrum Trading?

How Do Limitations in Spectrum Fungibility Impact Spectrum Trading?

Network dimensioning with carrier aggregation

A comprehensive spectrum trading scheme based on market competition, reputation and buyer specific requirements

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