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Current regulations leave a few television (TV) white spaces in populated urban areas where spectrum shortage is mostly experienced. As TV set feedback becomes essential in the next generation terrestrial TV standard, an opportunistic TV spectrum sharing based on TV receiver activity information and transmit power control is proposed to exploit the underutilized active TV channels. Based on investigation of the spatial–spectral–temporal characteristics of TV receiver activities, analytical models are developed to capture the spatio-temporal distributions of available spectrum and corresponding capacity. The influence of multiple factors, such as feedback delay, spectrum handover overhead, ranking order, and distribution of TV channel popularity are discussed and modeled. The proposed power control mechanism is verified through experiments at representative campus and residential environments. Empirical data-based simulations and geographic analyses are conducted to evaluate the developed models and further profile the spectrum opportunities within a cell, across New York city (NYC) and other 273 cities in the United States. In NYC, the proposed solution provides a 3.8 – 11.7 -fold increase of average spectrum availability, and 2.5 – 6.6 -fold increase of capacity from current regulations. By investigating the feasibility and prospects of this approach, this paper intends to motivate further discussions in policy, business, and privacy aspects to reach its significant potential. 

Problem definition: Inspired by new developments in dynamic spectrum access, we study the dynamic pricing of wireless Internet access when demand and capacity (bandwidth) are stochastic. Academic/practical relevance: The demand for wireless Internet access has increased enormously. However, the spectrum available to wireless service providers is limited. The industry has, thus, altered conventional license-based spectrum access policies through unlicensed spectrum operations. The additional spectrum obtained through these operations has stochastic capacity. Thus, the pricing of this service by the service provider has novel challenges. The problem considered in this paper is, therefore, of high practical relevance and new to the academic literature. Methodology: We study this pricing problem using a Markov decision process model in which customers are posted dynamic prices based on their bandwidth requirement and the available capacity. Results: We characterize the structure of the optimal pricing policy as a function of the system state and of the input parameters. Because it is impossible to solve this problem for practically large state spaces, we propose a heuristic dynamic pricing policy that performs very well, particularly when the ratio of capacity to demand rate is low. Managerial implications: We demonstrate the value of using a dynamic heuristic pricing policy compared with the myopic and optimal static policies. The previous literature has studied similar systems with fixed capacity and has characterized conditions under which myopic policies perform well. In contrast, our setting has dynamic (stochastic) capacity, and we find that identifying good state-dependent heuristic pricing policies is of greater importance. Our heuristic policy is computationally more tractable and easier to implement than the optimal dynamic and static pricing policies. It also provides a significant performance improvement relative to the myopic and optimal static policies when capacity is scarce, a condition that holds for the practical setting that motivated this research.