A Causal Model Analysis of Spectrum Management Policies

A Causal Model Analysis of Spectrum Management Policies

Varadharajan Sridhar (Sasken Communication Technologies, India), Thomas Casey (VTT Technical Research Centre, Finland) and Heikki Hämmäinen (Aalto University, Finland)
DOI: 10.4018/978-1-4666-4715-2.ch004
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Demand for wireless data and Internet services are expected to grow exponentially, worldwide, in the near future. While regulators of advanced countries have often used centralized planning and coordination approach to forecast and allocate the associated spectrum blocks to wireless operators for meeting the demand, it is often ad-hoc in emerging markets dictated by market forces. In this chapter, the authors construct a casual model to represent the different variables that affect spectrum management practices. The authors discuss possible policy options including release of digital dividend spectrum, spectrum refarming, and secondary markets consisting of spectrum leasing, sharing, and trading. Using the causal model structure, the authors trace paths of evolution of spectrum policies, taking an example each for developed and emerging countries. The authors further hypothesize that emerging countries with their unique market structure, legacy of spectrum management, and ex-poste regulation are better suited to create active secondary markets compared to developed markets.
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In a recent research study by Cisco (2010), it has been pointed out that Mobile data traffic will grow at a compound annual growth rate (CAGR) of 92 percent from 2010 to 2015, reaching 6.3 exabytes per month by 2015. The study also points out that “mobile-only Internet” population will grow 56-fold from 14 million at the end of 2010 to 788 million by the end of 2015. These trends clearly indicate the possible exponential growth in the use of mobile devices to access Internet and other related bandwidth intensive applications and services, especially in emerging markets such as India.

The potential increase in demand for wireless data and Internet services is likely to put stress on the wireless networks and hence the need for better spectrum management. Paucity of spectrum for commercial mobile services in emerging markets has been highlighted by many researchers (Hazlett, 2006). For example, the formulation of spectrum policy in India began under conditions of very limited availability of spectrum, due to huge spectrum holdings by Defense as indicated in Prasad & Sridhar (2009). There is the obvious trade-off before the policy maker, between the number of operators to be allocated spectrum and spectrum block allocated to each operator. In emerging countries such as India, the decision is made in favor of competition and hence the associated maximal usage of allotted spectrum. Even if many operators are present, the huge population and hence the potential user base for mobile services, is expected to provide each operator with the critical mass required for sustainable operation. The mobile subscriber growth in India as presented in Appendix 1 (Figure 4) and the level of competition as presented in Appendix 2 (Figure 5) illustrate these effects. Figure 4 also illustrates how there is a continued decline in fixed line subscription indicating possible substitution effect of mobiles.

As it is typical in advanced markets, the user base is not large enough to warrant many operators. Even in the U.S., there are only four carriers providing cellular mobile services nation-wide. The market share of local and regional carriers in any of the Cellular Market Areas in the US is still insignificant as pointed out in US-DoJ (2011). Hence the policies are always in favor of a limited number of operators with more spectrum blocks for each operator. It is still in the radar of US Federal Communication Commission (FCC) to cap spectrum per operator and limit allocation to certain threshold number of operators (US-GAO, 2011). Given this disparity in spectrum policies and market structure in emerging and advanced markets, it is interesting to analyze the future evolution path for spectrum management in these two extreme scenarios.

Sridhar and Hämmäinen (2011) indicate that the mobile Internet users in India have jumped from 8 million in 2010 to over 50 million and that about 49 percent of Internet users use Mobile only for accessing the Internet. Hence, on the demand side, the large number of mobile subscribers who can potentially access Internet and other broadband services using mobile only increases the demand. This poses stress on appropriate spectrum management. In emerging countries such as India, spectrum management challenges are due to (i) lack of alternative wired access network infrastructure for broadband access; (ii) deeper penetration of mobile phones and hence the associated demand for wireless broadband (iii) relatively inadequate allocation of spectrum for mobile services compared to advanced markets.

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