Investment and Pricing with Spectrum Uncertainty: A Cognitive Operator's Perspective

This paper studies the optimal investment and pricing decisions of a cognitive mobile virtual network operator (C-MVNO) under spectrum supply uncertainty. Compared with a traditional MVNO who often leases spectrum via long-term contracts, a C-MVNO can acquire spectrum dynamically in short-term by both sensing the empty "spectrum holes" of licensed bands and dynamically leasing from the spectrum owner. As a result, a C-MVNO can make flexible investment and pricing decisions to match the current demands of the secondary unlicensed users. Compared to dynamic spectrum leasing, spectrum sensing is typically cheaper, but the obtained useful spectrum amount is random due to primary licensed users' stochastic traffic. The C-MVNO needs to determine the optimal amounts of spectrum sensing and leasing by evaluating the trade off between cost and uncertainty. The C-MVNO also needs to determine the optimal price to sell the spectrum to the secondary unlicensed users, taking into account wireless heterogeneity of users such as different maximum transmission power levels and channel gains. We model and analyze the interactions between the C-MVNO and secondary unlicensed users as a Stackelberg game. We show several interesting properties of the network equilibrium, including threshold structures of the optimal investment and pricing decisions, the independence of the optimal price on users' wireless characteristics, and guaranteed fair and predictable QoS among users. We prove that these properties hold for general SNR regime and general continuous distributions of sensing uncertainty. We show that spectrum sensing can significantly improve the C-MVNO's expected profit and users' payoffs.Comment: A shorter version appears in IEEE INFOCOM 2010. This version has been submitted to IEEE Transactions on Mobile Computin

Resource allocation in realistic wireless cognitive radios networks

Cognitive radio networks provide an effective solution for improving spectrum usage for wireless users. In particular, secondary users can now compete with each other to access idle, unused spectrum from licensed primary users in an opportunistic fashion. This is typically done by using cognitive radios to sense the presence of primary users and tuning to unused spectrum bands to boost efficiency. Expectedly, resource allocation is a very crucial concern in such settings, i.e., power and rate control, and various studies have looked at this problem area. However, the existing body of work has mostly considered the interactions between secondary users and has ignored the impact of primary user behaviors. Along these lines, this dissertation addresses this crucial concern and proposes a novel primary-secondary game-theoretic solution which rewards primary users for sharing their spectrum with secondary users. In particular, a key focus is on precisely modeling the performance of realistic channel models with fading. This is of key importance as simple additive white Gaussian noise channels are generally not very realistic and tend to yield overly optimistic results. Hence the proposed solution develops a realistic non-cooperative power control game to optimize transmit power in wireless cognitive radios networks running code division multiple access up-links. This model is then analyzed for fast and slow flat fading channels. Namely, the fading coefficients are modeled using Rayleigh and Rician distributions, and closed-form expressions are derived for the average utility functions. Furthermore, it is also shown that the strategy spaces of the users under realistic conditions must be modified to guarantee the existence of a unique Nash Equilibrium point. Finally, linear pricing is introduced into the average utility functions for both Rayleigh and Rician fast-flat fading channels, i.e., to further improve the proposed models and minimize transmission power for all users. Detailed simulations are then presented to verify the performance of the schemes under the proposed realistic channel models. The results are also compared to those with more basic additive white Gaussian noise channels

Optimal resource allocation in femtocell networks based on Markov modeling of interferers' activity

Femtocell networks offer a series of advantages with respect to conventional cellular networks. However, a potential massive deployment of femto-access points (FAPs) poses a big challenge in terms of interference management, which requires proper radio resource allocation techniques. In this article, we propose alternative optimal power/bit allocation strategies over a time-frequency frame based on a statistical modeling of the interference activity. Given the lack of knowledge of the interference activity, we assume a Bayesian approach that provides the optimal allocation, conditioned to periodic spectrum sensing, and estimation of the interference activity statistical parameters. We consider first a single FAP accessing the radio channel in the presence of a dynamical interference environment. Then, we extend the formulation to a multi-FAP scenario, where nearby FAP's react to the strategies of the other FAP's, still within a dynamical interference scenario. The multi-user case is first approached using a strategic non-cooperative game formulation. Then, we propose a coordination game based on the introduction of a pricing mechanism that exploits the backhaul link to enable the exchange of parameters (prices) among FAP's