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Adoption of dynamic spectrum access technologies: a system dynamics approach

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Abstract

The introduction of dynamic spectrum access (DSA) technologies in mobile markets faces technical, economic and regulatory challenges. This paper defines industry openness and spectrum centralization as the two key factors that affect the adoption of DSA technologies. The adoption process is analyzed employing a comprehensive System Dynamics model that considers the network and substitution effects. Two possible scenarios, namely operator-centric and user-centric adoption of DSA technologies are explored in the model. The analysis indicates that operator-centric DSA technologies may be adopted in most countries where spectrum is centralized, while end-user centric DSA technologies may be adopted in countries with decentralized spectrum regime and in niche emerging services. The study highlights the role of standards-based design and concludes by citing case studies that show the practicality of this analysis and associated policy prescriptions.

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Notes

  1. Operator providing wireless internet access in a local basis, such as described in [2].

  2. This framework has been adopted by the US and the EU as a three tiered framework authorization [18].

  3. For Bass model description and application, see [42].

  4. Critical mass is the “minimum network size that can be sustained in equilibrium” [44].

  5. According to [54], spectrum license is an entry barrier according to Bain’s definition, but not according to Stigler’s definition. Considering that currently spectrum licenses have reselling rights and spectrum regimes are becoming more flexible, this study follows Stigler’s definition. Therefore spectrum licenses is not part of industry openness, but it is considered in a separate variable (spectrum centralization). A similar situation happens with taxi licenses, as explained by Demsetz [55].

  6. Quasi rent is a return of a firm, which is temporal in its nature due to e.g. temporal entry barriers. Appropriable quasi rent arise from a vertical integration or a transaction-specific investment.

  7. Under network effect, the number of adopters increases the value of a new adopter, since there are more connected devices to interact. In DSA, compatibility issues are relevant at both device and network sides.

  8. Low market concentration increases the probability of having spectrum transactions at retail level, since it provides the end-user buying power and increased service offer. If market concentration (and spectrum concentration) is higher, there will be less transactions at retail level (less switching possibilities for end-users), but operators instead may develop a wholesale market, through cooperative or market based mechanisms, if they see it beneficial. Note that under dominant position (i.e. monopoly), the probability of having transactions decreases at both levels.

  9. The literature has identified an inverted U-relationship between competition and investment [81, 82], in which investment is low with too high and too little competition. This relation has been widely acknowledged by more recent authors, such as [83, 84].

  10. This exercise models user-centric devices substituting operator-centric devices. However, it may be easily extrapolated to include other scenarios, such as an operator-centric DSA process substituting an older process without DSA.

  11. This competition coefficient affects the speed of convergence in the simulation results. This coefficient is in line with the one utilized in similar simulation exercises for telecom service substitution [86, 87] and was adjusted to converge for all the simulated scenarios.

  12. The value of a network can be described as \(N^{2}\) by Metcalfe’s law or as \(e^{N}\) by Reed’s law.

  13. Sources: [96, 97] and [98].

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Acknowledgments

This work has been partially funded by the End-to-End Cognitive Radio testbed project of Aalto University, which is part of the Tekes TRIAL program. Authors thank to Kalle Ruttik and to anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Department of Communications and Networking, Aalto University School of Electrical Engineering, PO Box 13000, 00076, Aalto, Finland

    Arturo Basaure & Heikki Hämmäinen

  2. Department of Industrial and Systems Engineering, Pontificia Universidad Católica de Chile, Avda. Vicuña Mackenna 4860, 7820436, Macul, Santiago, Chile

    Arturo Basaure

  3. Centre for Information Technology and Public Policy (CITAPP) International Institute of Information Technology Bangalore, No. 26/C, Electronic City, Hosur Road, Bangalore, 560 100, India

    Varadharajan Sridhar

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Correspondence to Arturo Basaure.

Appendices

Appendices

1.1 Appendix 1: Formulation of network and substitution effects

Under high network effect, path dependence implies that technologies rapidly lock-in to a stable equilibrium after reaching a critical mass. The attractiveness of each product is determined by several elements, one being the effect of compatibility on attractiveness of the technology due to the network effect [79], which can be described by an exponential functionFootnote 12:

$$\begin{aligned} \hbox {Eca}\left( \hbox {t} \right) =e^{{{\mathrm{se}}*\mathrm{b} }\,\left( {{\mathrm{t}}-1} \right) /{ \mathrm{th}}}, \end{aligned}$$

where Eca(t)= effect of compatibility on attractiveness at t, se = sensitivity of attractiveness; b(t-1) = installed base of devices at \(t-1\), and th = threshold for compatibility effect.

Then, the technology attractiveness is also affected by the investment incentives which are determined by the market structure:

$$\begin{aligned} \hbox {Aud }\left( \hbox {t} \right) =\hbox {Eca }\left( {\hbox {t}-1} \right) \,*\,\hbox {Ii}\left( {\hbox {t}-1} \right) , \end{aligned}$$

where Aud(t) = Attractiveness of devices at t, Eca(t-1) = Effect of compatibility on attractiveness of devices at \(t-1\), and Ii(t-1) = incentives to invest at \(t-1\).

During the simulation time, the model accumulates the number of adopted devices, which is calculated by the following integral:

$$\begin{aligned} \hbox {Ib}\left( \hbox {t} \right) =Ibo+\int sa\left( t \right) dt, \end{aligned}$$

where Ib(t) = Installed base of devices at t, sa(t) = sales of devices at t; Ibo: initial installed base of devices.

The first formula indicates that the number of devices should reach a threshold value to positively impact the attractiveness of the technology. In addition to this, other compatibility elements may also impact attractiveness. Market share is determined by the attractiveness of the technology divided by the total attractiveness of all competing technologies. This relies on the assumption that these two competing technologies are not complement.

Under substitution effect, a new technology substitutes an older one. The substitution effect can be described through a predator-prey competition model by means of the Lotka-Volterra equations which are shown as follows [48]:

$$\begin{aligned} \frac{dM}{dt}= & {} a_M M-b_M M^{2}\pm c_{ME} EM,\\&a_M >0, b_M >0, c_{ME} >0\\ \frac{dE}{dt}= & {} a_E E-b_E E^{2}\pm c_{EM} ME,\\&a_E >0, b_E >0, c_{EM} >0, \end{aligned}$$

where a is the growth or positive feedback from the adoption, b is the inhibition or saturation coefficient, which describes the loss of potential market due to the growth of a technology, and therefore can be expressed as growth rate/capacity. Finally c is the competition coefficient between two technologies (E and M in this case). If \(c_{EM}\) is positive, the technology E influences positively the technology M. If \(c_{EM}\) is negative, the technology E influences negatively the technology M. If \(c_{EM}\) is zero, the influence is neutral. In a predator-prey scenario, the c coefficient of the prey is positive, while the c coefficient of the predator is negative.

These models are adopted in Figs. 3 and 4 by utilizing causal loop diagrams.

1.2 Appendix 2: Parameter description of system dynamics models

Network effect model

Variable

Value

Spectrum centralization

from 0 to 1

Industry openness

from 0 to 1

Incentives to invest

from 0 to 1

Threshold for spectrum sharing

25 %\(^{\mathrm{a}}\)

Initial amount of spectrum available for user access

5 %

Initial base of user-centric devices

100

Initial base of operator-centric devices

100

Substitution model

Variable

Value

Spectrum centralization

from 0 to 1

Industry openness

from 0 to 1

Incentives to invest

from 0 to 1

Threshold for user-centric spectrum access

25 %

Initial amount of spectrum available for user access

5 %

Initial base of operator-centric devices

90 %

Initial base of user-centric devices

10 %

Competition effect user-centric devices

\(-\)0.02

Competition effect operator-centric devices

0.02

  1. \(^{\mathrm{a}}\) Amongst the OECD countries [96], the average market share of the top four MNOs is 43, 31, 19 and 6 % respectively, yielding an average market share of about 25 %. Using this data, we have considered the minimum threshold for entry of an MNO to be approximately 5 % (the fourth market share). The average of the top four MNOs (25 %) is considered as indicative of the average critical size for sustainable equilibrium, utilized as a threshold value of available spectrum for user-centric adoption

1.3 Appendix 3: Country data

List of selected countries with variables describing spectrum centralization and industry opennessFootnote 13:

Countries

Finland

India

Japan

United States

Market concentration (HHI)

0.332

0.186

0.348

0.247

Spectrum concentration (HHI)

0.327

0.131

0.347

0.287

Reselling rights

Yes

No

No

Yes

Mobile monthly ARPU

32

3

84

47

Churn rate (% monthly)

1,60

5,80

0,60

1,80

Cellular investment per capita per year (USD)

34,33

6,05

134,49

77,91

Price (USD per minute)

0,14

0,04

0,63

0,39

Fraction of prepaid subscriptions (%)

10

95

1

22

Separation of network and service operators

Yes

No

No

No

Number of MNOs (per service area or nationwide)

3

10

3

6

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Basaure, A., Sridhar, V. & Hämmäinen, H. Adoption of dynamic spectrum access technologies: a system dynamics approach. Telecommun Syst 63, 169–190 (2016). https://doi.org/10.1007/s11235-015-0113-7

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  • DOI: https://doi.org/10.1007/s11235-015-0113-7

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