مدیریت طیف های انعطاف پذیر برای خدمات باند پهن تلفن همراه: این مدیریت چگونه در بازارهای پیشرفته و نوظهور می تواند متفاوت باشد؟
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|13753||2013||14 صفحه PDF||سفارش دهید||10282 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Telecommunications Policy, Volume 37, Issues 2–3, March–April 2013, Pages 178–191
Demand for wireless data and Internet services are expected to grow exponentially, both in advanced and emerging markets in the near future. While advanced countries have often used centralized planning and coordination methodology 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 paper, Finland and India are taken to represent advanced and emerging markets, respectively. Different policy options and the policy environment in these two countries for spectrum management are explored. A causal model is constructed to represent the different variables that affect spectrum management practices and possible paths forward in these two extreme cases are highlighted. Using the causal model structure, it is hypothesized that (i) the matured markets such as Finland that practice centralized and harmonized spectrum planning are likely to continue their ex-ante policies and opt for the release of digital dividend spectrum and use of spectrally efficient technologies; (ii) the emerging market in India that is characterized by a market oriented ex-poste regulation is a good candidate to introduce secondary markets including flexible opportunistic spectrum access as exemplified by the wide spread adoption of multi-SIM handsets and the practice of national roaming by 3G service providers. Introductions of policies and regulations in these markets to break away from the extant paths are also highlighted.
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% 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. 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 and 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. However, typically in advanced countries, the user base is not large enough to warrant many operators. Hence the policies are always in favor of a limited number of operators with more spectrum blocks for each operator. 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. In this paper the authors provide a detailed analysis of the issues in spectrum management, for India and Finland that represent, respectively, emerging and developed countries in telecommunications. Basic telecom characteristics of these two countries are presented in Table 1. Table 1. Comparison of economic, technology and network readiness indicators (source: Dutta and Mia, 2011 and World Bank, 2010). Indicator India Finland Network readiness Overall network readiness index 4.0 (48th rank) 5.4 (3rd rank) No. of landlines per 100 population 3.1 26.9 No. of mobile subscriptions (in millions) 630 (June 2011) 7.8 No. of mobile subscription per 100 population 43.8 144.6 Mobile network coverage (% population covered) 83.0 99.5 Cellular subscriptions with data (% of total) About 4 100 Mobile cellular tariff (in PPP $) 0.06 0.17 No. of Internet users per 100 population 5.1 82.5 No. of broadband Internet subscribers per 100 population 0.6 28.8 International Internet bandwidth per 10,000 population in Mbps 2.2 172.2 Internet and telephony competition (0–6; 6 best) 6.0 6.0 Availability of latest technologies (1–7; 7 most) 5.6 6.6 Economic and demographics GDP per capita (constant US$ 2000) 823 27,314 Population (millions) 1,171 5.3 Population ages 15–64 (% of total) 64.5 66.2 Rural population (% of total) 69.9 36.1 Annual population growth (%) 1.34 0.46 Population density (people per sq.km of land area) 394 17.65 Population in the largest city (in million) 22 1.1 ICT services export (current US $ billion) 58 6.8 Government characteristics relating to ICT Government prioritization of ICT (1–7; 7 best) 5.3 6.1 E-participation in government (0–1; 1 best) 0.2 0.41 ICT use and government efficiency (1–7; 7 best) 4.7 5.3 Table options A quick look at the above table indicates that apart from cellular mobile services, India lags behind Finland in all the parameters. Apart from the density parameters, the absolute number of mobile subscriber numbers in emerging countries such as India (about 850 million, largest in the world second only to China) give the required economies of scale and scope of operation for operators, device vendors, original equipment manufacturers, and content providers alike. The demographic indicators point to a large number of working population (about 750 million) in India who are potential adopters of Smartphones, tablets and associated wireless broadband services. Though the GDP per capita is much less in India compared to Finland, the availability of latest technologies in India is not far behind that of Finland, which indicates the possibility of a section of the population actively using and adopting latest technologies. It is estimated that Smartphone sales in India is expected to increase from the current 21 million per year to 100 million per year by 2015, mainly due to adoption by the population in the age group of 15–64. Sridhar and Hämmäinen (2011) indicate that the mobile Internet users in India have jumped from 8 million last year to 25 million and that about 49% 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 practices. In emerging countries such as India, spectrum management challenges are due to (i) lack of alternative wired access network infrastructure for broadband service; (ii) deeper penetration of mobile phones and hence the associated demand for wireless broadband (iii) inadequate allocation of spectrum for mobile services. In developed countries such as Finland, challenges are due to (i) increase in demand for wireless data services as indicated by the network readiness index and the cellular subscriptions for data services and (ii) adoption of inclusive broadband strategy. A critical parameter to note in the above table is the mobile cellular tariffs in India which is one of the lowest in the world. Due to intense competition, the operators provide usage based variable pricing plan that cuts across different subscriber segments. Subscribers have started using multi-subscriber identification module (SIM) phones to take advantage of the various pricing models. However, in Finland, the operators started offering mobile broadband services at flat rates due to (i) enough spectrum and network capacity compared to demand (ii) users accustomed to flat rate pricing in wired networks and hence had the propensity to expect the same in wireless networks. In flat rate scenario, subscribers maximize their usage for the fixed tariff they pay. Hence, in a flat rate pricing model, traffic shaping and capacity management by the operators assume importance. A look at the indicators also point to the increased contribution of the information and communications technology (ICT) sector in India (viz. more than 7% of country’s GDP), as given by the high ICT export revenue. However, it must be pointed out that the above is mainly due to private enterprises as is indicated by the low values of e-participation of government and government’s ICT orientation. However, in Finland, there is relatively higher adoption of ICT by the government and its enhanced prioritization of ICT growth. Given the above differences, in this paper, it is the authors objective to identify important policy variables which are of importance in the context of spectrum management and the relationship amongst them across these two countries. The different variables are identified using their prior research work, policy and regulatory documents. the authors also conducted structured interviews with select stakeholders of the ecosystem, namely officials in appropriate government departments and regulators, managers of the mobile operators, and spectrum brokerage companies. System dynamics is a commonly used tool to study causal relationships and the resulting endogenous structure amongst variables of interest (Sterman, 2000). It has been widely applied in policy analysis and has also been used in the mobile communications context both for understanding market development (Pagani and Fine, 2008 and Jain and Sridhar, 2003) and regulatory policy (Casey & Töyli, 2011). The authors use system dynamics methodology to identify the cause and effect relationship amongst variables of interest using a stakeholder (in this case, regulator and policy maker) view point. Using the causal model they hypothesize the possible policy paths for spectrum management in Finland and India. Rest of the paper is organized as follows. In the next section they compare the spectrum management principles and polices in Finland and India. In Section 3, they propose possible spectrum policy options for the two representative countries given their policy orientation and legacy. The system dynamics causal model indicating the cause-effect relationship amongst the different variables of interest is presented in Section 4. They conclude the paper with the discussions on how the advanced and emerging market regulations are poised for the future.
نتیجه گیری انگلیسی
Taking in to account the observations made in the earlier sections and the nature of balancing and reinforcing loops in the causal diagrams, the authors can hypothesize that the harmonization policy pursued in the advanced markets such as Finland has led to a situation where demand for wireless broadband in the future will be met mainly by increasing adoption of spectrally efficient technologies (i.e., refarming of UMTS and LTE technologies on legacy bands) and by releasing more digital dividend spectrum for the exclusive use of the MNOs. The policies pursued in emerging countries such as India and the corresponding market structure that has emerged, in turn, could lead to higher activity in the secondary markets both in terms of co-operative trading and sharing between the MNOs and MVNOs/ISPs/venue owners and opportunistic spectrum access by the end-users, and thus to a more rapid diffusion of cognitive radio technologies. As indicated in Fig. 3, cost of devices play a very important role in the adoption of cognitive radio technologies. The market size is large enough to provide the required scale economies to drive down the prices and hence increase adoption. Since the established path dependencies are strong and the underlying structures that have caused them are typically quite slow to change, it is more likely that both markets are expected to continue on their current paths. However, it must also be pointed out that regulators and policy makers are continuously exploring ways to break away from the legacy and initiate policy directives as per the continuously evolving technology and market conditions. The authors discuss in this section our observations on the validity of their model and hypotheses.