Pricing of spectrum based on physical criteria

Kerans, Andrew; Vo, Dat; Conder, Phillip; Krusevac, Snezana

doi:10.1109/dyspan.2011.5936210

Cited by 15 publications

(7 citation statements)

References 12 publications

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“…Radio frequency is a crucial determinant of spectrum value. Lower frequencies allow a signal to transmit farther distances, theoretically requiring a smaller number of overall stations to carry data across the same distance as higher frequencies (Kerans et al, 2011). Additionally, lower band spectrum benefits from lower attenuation, meaning signals are less disrupted when encountering obstacles such as buildings and landmarks, constituting an additional property which increases its range.…”

Section: Iva License Specific Traitsmentioning

confidence: 99%

The Evolution of U.S. Spectrum Values Over Time

et al. 2018

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Section: Iva License Specific Traitsmentioning

confidence: 99%

The Evolution of U.S. Spectrum Values Over Time

et al. 2018

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“…Hence, these physical propagation effects are often accounted for in spectrum auction prices where license costs reflect the ability of different frequencies to provide costeffective deployments. Similarly, the economic value of 3.5 GHz spectrum would be almost one tenth of 850 MHz when accounting for propagation characteristics [23]. Nonetheless, the evolutionary path of each MNO does affect generational upgrades, whereby past deployment strategies can affect the future price an MNO may be willing to pay for new spectrum, due to complementarity and substitutability across bands.…”

Section: Introductionmentioning

confidence: 99%

How Does Spectrum Affect Mobile Network Deployments? Empirical Analysis Using Crowdsourced Big Data

2020

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Despite the growing trend towards the use of big data methodologies, there is still limited application of such techniques to understand how spectrum is used in mobile networks. In this paper we analyse how low (<1 GHz) and high (>1 GHz) frequency spectrum is used in 4G networks in urban areas, in relation to eNodeB density, available bandwidth, Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ). We present a method to analyse the strategies used by Mobile Network Operators (MNOs) to deal with traffic congestion, and the degree to which they must densify their networks depending on their spectrum portfolio. Using crowdsourced data from 2017 from a popular mobile app, we apply this method to Greater London. We find that the fraction of sites that fully use all available bands to the MNO range from 2% to 20%. Additionally, MNOs with large bandwidth use 42% fewer sites on average in dense urban environments. This difference decreases in suburban areas to 23% fewer sites. The lowest frequencies in each eNodeB tend to exhibit lower RSRP values, as they are often used to serve cell-edge users. These frequencies also show lower RSRQ values because of higher interference caused by neighbouring cells. Similarly, large (high frequency) bandwidth improves RSRQ as it allows for fewer users per MHz, which reduces interference and enables larger cell sizes. We conclude that in dense urban environments, the available bandwidth, rather than propagation properties, determines the preferred band for network deployment by MNOs. INDEX TERMS 4G mobile communication, radio spectrum management, crowdsourcing

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“…Kerans et.al. [7] showed that the coverage differences described above affect prices in the primary (auction) market outcomes. In part, the differences in auction prices are due to the larger infrastructure investment requirements to cover the same area with higher frequency channels.…”

Section: A Spatialmentioning

confidence: 99%

“…Is one slice exactly identical to another slice of the same size, but at a different frequency (i.e., will it provide the same communication capabilities)? While for example, Kwerel and Williams assert that frequencies in the 300 MHz to 3000 MHz band are "approximately fungible" [4], more recent research indicates different auction prices obtained for different frequencies in this band [7] in practice, indicating that the economic value is not the same for different slices of spectrum. Outside of this economic evidence, we examine technical and practical issues related to the fungibility of spectrum, such as the allowed transmit power and physical layer alternatives, the propagation characteristics at different frequencies, the interference features, the capabilities of the communicating devices, and the application needs since they all influence the communication capabilities afforded by a slice of spectrum at a specific frequency.…”

Section: Introductionmentioning

confidence: 99%

When is electromagnetic spectrum fungible?

Weiss

Krishnamurthy

Doyle

et al. 2012

2012 IEEE International Symposium on Dynamic Spectrum Access Networks

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Abstract-Fungibility is a common assumption for marketbased spectrum management. In this paper, we explore the dimensions of practical fungibility of frequency bands from the point of view of the spectrum buyer who intends to use it. The exploration shows that fungibility is a complex, multidimensional concept that cannot casually be assumed. We develop two ideas for quantifying fungibility -(i) of a fungibility space in which the "distance" between two slices of spectrum provides score of fungibility and (ii) a probabilistic score of fungibility.

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Pricing of spectrum based on physical criteria

Cited by 15 publications

References 12 publications

The Evolution of U.S. Spectrum Values Over Time

The Evolution of U.S. Spectrum Values Over Time

How Does Spectrum Affect Mobile Network Deployments? Empirical Analysis Using Crowdsourced Big Data

When is electromagnetic spectrum fungible?

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