Competitive Spectrum Pricing under Centralized Dynamic Spectrum Allocation

Competitive Spectrum Pricing under Centralized Dynamic Spectrum Allocation

Hailing Zhu, Andre Nel, Hendrik Ferreira
DOI: 10.4018/978-1-4666-6571-2.ch034
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Dynamic Spectrum Allocation (DSA) has been viewed as a promising approach to improving spectrum efficiency. With DSA, Wireless Service Providers (WSPs) that operate in fixed spectrum bands allocated through static allocation can solve their short-term spectrum shortage problems resulting from the bursty nature of wireless traffic. Such DSA mechanisms should be coupled with dynamic pricing schemes to achieve the most efficient allocation. This chapter models the DSA problem where a centralized spectrum broker manages “white space” in the spectrum of TV broadcasters and sells the vacant spectrum bands to multiple WSPs, as a multi-stage non-cooperative dynamic game. Furthermore, an economic framework for DSA is presented and a centralized spectrum allocation mechanism is proposed. The simulation results show that the centralized spectrum allocation mechanism with dynamic pricing achieves a DSA implementation that is responsive to market conditions as well as enabling efficient utilization of the available spectrum.
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Currently, the radio spectrum is regulated by governmental authorities (e.g. FCC in USA or ICASA in South Africa) under the “command-and-control” spectrum management policy, in which radio spectrum is statically allocated to license operators over large geographical areas on a long term basis (years or even decades) along with rigid specification of the usage parameters (e.g. power, geographical scope) strictly for specific purposes (e.g. broadcast radio/TV, cellular services, wireless LAN). License operators get exclusive access to some specific spectrum bands which are licensed to them. The advantage of such a static spectrum allocation approach is that the interference between neighboring systems is successfully and completely avoided.

However, with the spectrum utilization in cellular or other wireless networks varying spatially and temporally, the static allocation of spectrum exhibits certain limitations. It has been observed that the perceived scarcity of radio spectrum is mainly due to the inefficiency of static spectrum allocation policies (FCC Spectrum Policy Task Force, 2002). Spectrum occupancy measurements showed that temporal and geographical variations in the utilization of the licensed spectrum range from 15% to 85%, and that a large portion of licensed spectrum is severely under-utilized, e.g. over 60% of the licensed spectrum below 6 GHz remains unused or substantially underutilized (FCC Spectrum Policy Task Force, 2002). In addition, spectrum utilization measurements of 30 frequency bands from 30MHz to 2.9GHz showed that average spectrum occupancy was estimated to be no more than 17.4% while occupancy varies from less than 1% to 70.9% in densely populated cities such as Chicago (McHenry, McCloskey, Roberson, & MacDonald, 2005). The long-term command-and-control model of spectrum allocation not only results in under-utilization of spectrum for protracted periods of time in areas of low demand but also leads to slow network infrastructure and service deployments (Subramanian, Gupta, Das, & Buddhikot, 2007).

With the growth of wireless communications that resulted from the dramatic proliferation in the use of different wireless network technologies, such as cellular networks, WiFi, WiMAX, etc. as well as an increasing deployment of wireless applications with increasing bandwidth requirements, wireless networks require significantly more spectrum to accommodate the increasing demand for bandwidth of users. Under static spectrum allocation, which is very long-term and space-time invariant, if there is a higher demand for spectrum from the users than their statically allocated spectrum, wireless network operators are not able to purchase extra spectrum for a short period of time even if they are willing to pay for it.

Thus, to overcome this artificial spectrum scarcity and under-utilization dilemma, new communication techniques - Cognitive Radio (CR) and Dynamic Spectrum Sharing (DSS) have been introduced. The development of CR technologies in DSS has been viewed as an alternative to the traditional static spectrum management policy to improve inefficient static spectrum utilization by allowing unlicensed wireless networks (or users) to dynamically access the so-called “white spaces” in the spectrum owned by legacy spectrum holders based on short-term leasing agreements.

Key Terms in this Chapter

Spectrum Management: The combination of administrative, regulatory and technical procedures to promote optimal use of radio frequencies in social, economic and technical terms.

Game Theory: The study of mathematical models pertaining to the strategic interaction of decision making where several self-interested players must make choices that potentially affect the interests of other players.

Dynamic Spectrum Allocation: A more spectrum efficient technique where spectrum is dynamically assigned to users taking into account spatial and temporal traffic statistics of different services.

Dynamic Spectrum Sharing: A new communication technique that allows spectrum to be shared dynamically between primary users and secondary users in spatial and temporal domains to be more responsive to user demands to improve spectrum utilization.

White Space: Refers to portions of licensed radio spectrum that licensees do not use all of the time or in all geographical locations.

Spectrum Trading: A broad concept encompassing various means of introducing a “secondary market” where license operators are allowed to trade their exclusively assigned spectrum usage rights to unlicensed parties (i.e., cognitive radio operators), with the objective of enhancing the efficient use of spectrum.

Dynamic Pricing: Refers to the process of fluctuating prices in response to changing circumstances, such as changes in demand, network status or marketing conditions.

Cognitive Radio: An adaptive and intelligent radio that can be programmed and configured dynamically. It can automatically detect available channels in a wireless spectrum, then accordingly change its transmission parameters to enable more communications to run concurrently in a given spectrum band at one location and also improve radio operating behavior.

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