Spectrum Sharing as Options

19 Pages Posted: 31 Mar 2016 Last revised: 1 Sep 2016

See all articles by Liu Cui

Liu Cui

West Chester University Computer Science Department

Martin B. H. Weiss

University of Pittsburgh - School of Information Sciences

Date Written: March 29, 2016


Spectrum access policy largely impacts the business models for wireless carriers. The amount of revenue that wireless carriers can earn from (statically assigned and allocated) spectrum is generally the product of Average Revenue Per User (ARPU) and the number of subscribers. But revenue is also related to how “well” they use the spectrum, which is a combination of application type and their Quality of Services (QoS) level. One criterion for evaluating QoS is signal to noise and interference radio (SINR). When the interference level is high, the expected revenue may decrease with the decreases of QoS level. Consequently, the potential interference that is brought by users in adjacent bands in the same area is a significant concern for wireless carriers. In order to prevent this type of potential interference, large guard bands have been instituted at the cost of spectrum utilization efficiency.

The emerging spectrum environment is quite different from this static approach. Today, spectrum is assigned more dynamically, either through secondary spectrum markets or through other spectrum sharing mechanisms, such as the new Citizen’s Broadband Radio Services (CBRS) and other initiatives that allow private entities to share federal agencies’ frequency bands. Under spectrum sharing policy, new business models are emerging. Primary users (PUs) now have two sources of revenue: providing wireless services and leasing spectrum to secondary users (SUs). In this environment, potential interference that may be brought by users in the same/adjacent bands in the same area need not lead to business failure even though the QoS decreases. Now, PUs can balance the potential spectrum leasing gain with revenue loss due to low QoS, and then make an informed decision as to how to use their spectrum assets. On the other hand, SUs only generate revenue from providing wireless services, but they are not locked in to any static situation. For example, they can choose between opportunistic and cooperative spectrum sharing, or even the mix of the two. With spectrum sensing and software defined radio, they can also choose different frequency bands when channel becomes congested.

In this new world, a key success factor for wireless carriers is their ability to continually adapt to a changing spectrum utilization environment. In other words, they must exercise management flexibility in an environment of continuous decision making. For example, when service demand increases, they need a way to acquire more spectrum and then increase the revenue. On the other hand, when the service demand decreases, they need a way to manage their potential loss. This management flexibility has not received much attention from the research literature. (Weiss, 2012 & Cui, 2014) proposes spectrum trading as options to mitigate uncertainties in the future and incentivize PUs to enlarge the quantity of leasable spectrum. (Lehr, 2015) suggests priority access licenses (PALs) as options to exclude GAAs to ease the implementation and address the asymmetric information challenge confronted by regulators. (De Vries, 2015) suggests risk informed regulation which is based on explicit quantification of the changing spectrum utilization environment.

In this paper, we will use a real options model to analyze different spectrum usage approaches. Using this approach, we investigate the “potential” of one strategy considering future changes and capabilities of coping with risks. For example, for cooperative sharing through trading, a risk is that it is difficult if not impossible for PUs and SUs to predict their future usage. As a result, we anticipate that: spectrum users will be conservative. PUs may only lease the minimum amount of spectrum in case their future service demand will increase. SUs may also lease the minimum amount of spectrum just in case the demand will decrease; or spectrum users may lease the maximum amount of spectrum with a risk of service degradation or financial failure.

Neither solution is desirable. Trading spectrum as financial options can reduce the risks in this situation. These options give the buyer the right but not obligation to share the spectrum. This asymmetry provides protection for both parties. On one side, the buyer of the right can decide whether to share the spectrum or not depending on their situations. On the other side, the seller of the right either gain premium (spectrum leasing fee) when buyers exercise the right or strike (price of the right) when buyers do not exercise the rights.

Next, we broaden our view to all spectrum sharing methods from wireless services entrants’ perspective. Before they enter the wireless market, they face the problem of selecting the most appropriate spectrum usage method. Each spectrum usage method has embedded risks and uncertainties, such as changing situations in spectrum utilization environment, regulatory rules, and service demands, that may occur throughout the investment life cycle.

However, risks and uncertainties may not necessarily lead to failure. When they occur, instead of passively commit to the existing business strategy, corporations have the right to delay, expand, contract, or abandon a project with a given cost or salvage value at some future date. This management flexibility may be able to alleviate the risks. Hence, in order to reduce the possibility of business failure, a clear understanding of each spectrum usage method is essential. Therefore, we will identify potential risks and mitigation strategies for each spectrum usage method. Then, we will quantify the value of each spectrum sharing method considering both risks and mitigation strategies by real options. With this value, spectrum entrants can make an informed decision by explicitly considering risks and mitigation strategies.

The outcome of this paper will promote spectrum sharing by minimizing financial risks. It will also help understanding the potential problems for each spectrum sharing method. Therefore, policy makers, operators, and the spectrum market could create interventions in order to obtain the favorable outcomes.


Lehr, William. ”Spectrum License Design, Sharing, and Exclusion Rights.” Sharing, and Exclusion Rights (August 15, 2015) (2015). Weiss, Martin BH, and Liu Cui. ”Spectrum trading with interference rights.” Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), 2012 7th Interna- tional ICST Conference on. IEEE, 2012. Cui, Liu, Taieb Znati, and Martin BH Weiss. ”Coopetive Spectrum Trading--Creating Endogenous Spectrum Holes.” Mobile Networks and Applications 19.4 (2014): 451-466. De Vries, Jean Pierre. ”Risk-Informed Interference Analysis: A Quantitative Basis for Spectrum Allocation Decisions.” Available at SSRN 2574459 (2015).

Suggested Citation

Cui, Liu and Weiss, Martin B. H., Spectrum Sharing as Options (March 29, 2016). TPRC 44: The 44th Research Conference on Communication, Information and Internet Policy 2016. Available at SSRN: https://ssrn.com/abstract=2756167

Liu Cui (Contact Author)

West Chester University Computer Science Department ( email )

United States

Martin B. H. Weiss

University of Pittsburgh - School of Information Sciences ( email )

135 N Bellefield Ave
Pittsburgh, PA 15260
United States

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