How Do Limitations in Spectrum Fungibility Impact Spectrum Trading?

Gomez, Marcela; Weiss, Martin B. H.

doi:10.2139/ssrn.2241731

Cited by 6 publications

(14 citation statements)

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“…Examples of studies that have estimated the costs of building wireless networks with different spectrum resources include Oughton and Frias (2017), who provide a detailed cost-model for building out 5G small cell infrastructure across the UK; who consider the total cost of ownership of different portfolios of spectrum assets; Johansson et al (2007), who model the costs of supporting heterogeneous wireless networks; and Bouras et al (2015), who model the costs of dense cell deployments. Gomez and Weiss (2013) offer one of the few papers to examine how different technical features may limit spectrum substitutability, thereby reducing the fungibility of different spectrum rights.…”

Section: Estimating the Value Of Commercial Spectrum Licensesmentioning

confidence: 99%

Spectrum Valuation: Implications for Sharing and Secondary Markets

et al. 2018

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TPRC46 Abstract 1How much is electromagnetic spectrum worth? Appropriate metrics and methodologies for valuing spectrum help policymakers, network operators, service providers, and end-users in planning wireless-related investment and in ensuring that spectrum resources are used efficiently. Secondary markets have often served to provide publicly observable, market-based valuation metrics, but in the case of spectrum, these are under-developed and segmented, limiting the availability and comparability of market transactions as indicators of spectrum value. Furthermore, the continued growth in wireless services and networks of all types and further advances in wireless technologies enabling more dynamic and granular spectrum sharing are transforming the supply and demand conditions for RF spectrum.Today, the most common metric for valuing spectrum resources is $/MHz-POP, derived from dividing the value of a spectrum transaction by the total population in the coverage area of the license times the bandwidth (in MHz). Traditionally, spectrum value has been observed in spectrum auctions, M&A transactions involving the transfer of spectrum usage rights, or from infrequent secondary market activity. This was a viable approach when the fungibility of spectrum resources was limited by technical, market, and regulatory factors that constrained the commodification of highly differentiated spectrum resources and limited the potential for dynamically reallocating, substituting and transferring spectrum rights via markets.With increased opportunities for spectrum sharing, the transition to 5G, smaller cell architectures, and the emergence of IoT, new spectrum usage patterns are arising and enabling more granular, multi-dimensional, virtualized spectrum management (in terms of frequency, location, time, etc.). In a world of increasing spectrum sharing, dynamic spectrum access, and commercial applications of higher frequencies for wireless service, $/MHz-POP may be an increasingly noisy indicator of spectrum value.In this paper, we consider how changing technology, markets and policy are enabling the commoditization of spectrum resources and explore what that implies for traditional spectrum value metrics that are used to project auction proceeds and value spectrum transactions.

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Section: Estimating the Value Of Commercial Spectrum Licensesmentioning

confidence: 99%

Spectrum Valuation: Implications for Sharing and Secondary Markets

et al. 2018

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“…Fungibility studies are also very relevant to our work. Examples include [8], which introduced several methods to compute fungibility scores, and [40], where those scores are refined. A set of related works aims at the more general objective of assigning a value to wireless spectrum, for the purpose of introducing fluid networks [41], and enabling spectrum trading [42], [43].…”

Section: Related Workmentioning

confidence: 99%

Sensitivity Analysis on Service-Driven Network Planning

Francesco

Kibiłda

Malandrino

et al. 2017

IEEE/ACM Trans. Networking

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Abstract-Service providers are expected to play an increasingly central role in the mobile market and their relationship with the traditional mobile network operators (MNOs) is starting to change. The dilemma faced by over-the-top service-providers (OTT) is now whether to enter into a service level agreement with the MNOs (in the same spirit of mobile virtual network operator agreements) or to invest in deploying their own network infrastructure to serve their demand. The purpose of this paper is to study the factors shaping the agreements between OTTs and MNOs and how these factors impact network planning decisions. To this end, we build a synthetic model of cellular network deployment that explores how traditional mobile operators and OTTs compete in deploying new infrastructure. Using our model in conjunction with real-world data, we find that service-driven networks are heavily influenced by regulatory decisions, and that cost structures and demand characteristics play non-marginal roles in the definition of service-driven networks.

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“…Spectrum trading scenarios have already been analyzed under the conditions mentioned above [3,11], in which the trading commodity was considered to be "naked spectrum". In [11], electromagnetic spectrum was regarded as perfectly fungible, creating in this manner a completely homogeneous commodity.…”

Section: The Appropriate Trading Commoditymentioning

confidence: 99%

“…In [11], electromagnetic spectrum was regarded as perfectly fungible, creating in this manner a completely homogeneous commodity. By incorporating fungibility limitations and constraints to the model, in [3], we could determine how this affected the trading choices and patterns of users and thus the final market viability result. In both cases, liquidity outcomes were reached; not surprisingly, those of the second case were fewer than those of the first, homogeneous case.…”

Section: The Appropriate Trading Commoditymentioning

confidence: 99%

“…In both cases, liquidity outcomes were reached; not surprisingly, those of the second case were fewer than those of the first, homogeneous case. However, as presented in [3], the market scenarios associated with liquidity in the second case are difficult to envision in reality (e.g., market liquidity involves the participation of a higher number of secondary users than the number of participants in the current telecommunications market). Thus, we believe that it is critical to determine a spectrum-related commodity that would permit us to maintain the market thickness obtained when trading a homogeneous commodity; in other words, a commodity that would bypass the spectrum heterogeneity constraints.…”

Section: The Appropriate Trading Commoditymentioning

confidence: 99%

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Trading Wireless Capacity Through Spectrum Virtualization Using LTE-A

2014

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Markets for spectrum were first proposed by Ronald Coase [1] as a way to efficiently allocate this resource. It took another forty years for primary markets to be developed (in the form of spectrum auctions) as the mechanism for assigning spectrum licenses to users. It is not a secret that secondary markets would be necessary to fully realize the benefits of economic allocation of spectrum. But this is easier said than done, since spectrum is a complex, multi-dimensional product with relatively few buyers and sellers (at least for commercial mobile services), so liquid secondary markets have not emerged, even though spectrum trading through brokers is commonplace.In this paper, we find that liquidity for spectrum markets can be improved over "naked" spectrum markets [2, 3] when a standardized commodity can be traded that uses the principles of spectrum virtualization [4]. We utilize the Physical Resource Blocks (PRBs) of LTE-Advanced as the traded commodity and modify the SPEC-TRAD model developed in [5] accordingly. Though much remains to be done, we find that this is a promising approach to finally realizing liquid secondary markets in radio spectrum.

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How Do Limitations in Spectrum Fungibility Impact Spectrum Trading?

Cited by 6 publications

References 6 publications

Spectrum Valuation: Implications for Sharing and Secondary Markets

Spectrum Valuation: Implications for Sharing and Secondary Markets

Sensitivity Analysis on Service-Driven Network Planning

Trading Wireless Capacity Through Spectrum Virtualization Using LTE-A

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