Coopetition spectrum trading - Creating endogenous spectrum holes

Dynamic spectrum access is an important way to alleviate the spectrum scarcity. Traditionally, secondary users are allowed to opportunistically operate when primary users are absent and buy idle bands from primary users. Although it improves the spectrum utility to some extents, it is not enough. In this paper, we argue that spectrum hole should be endogenous which means they should be negotiated by primary and secondary users. We proposed a novel spectrum sharing scheme with spectrum usage and management right. Further, we suggest spectrum trading as a financial option to capture the realistic usage and increase the trading flexibility. We show the superior of our model for primary users in deterministic and dynamic leasing environments. © 2013 ICST - The Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Inter-Operator Spectrum Sharing from a Game Theoretical Perspective

International audienceWe address the problem of spectrum sharing where competitive operators coexist in the same frequency band. First, we model this problem as a strategic non-cooperative game where operators simultaneously share the spectrum according to theNash Equilibrium (NE). Given a set of channel realizations, several Nash equilibria exist which renders the outcome of the game unpredictable. Then, in a cognitive context with the presence of primary and secondary operators, the inter-operator spectrum sharing problem is reformulated as a Stackelberg game using hierarchy where the primary operator is the leader. The Stackelberg Equilibrium (SE) is reached where the best response of the secondary operator is taken into account upon maximizing the primary operator's utility function. Moreover, an extension to the multiple operators spectrum sharing problem is given. It is shown that the Stackelberg approach yields better payoffs for operators compared to the classical water-filling approach. Finally, we assess the goodness of the proposed distributed approach by comparing its performance to the centralized approach

Applications of Game Theory and Microeconomics in Cognitive Radio and Femtocell Networks

Cognitive radio networks have recently been proposed as a promising approach to overcome the serious problem of spectrum scarcity. Other emerging concept for innovative spectrum utilization is femtocells. Femtocells are low-power and short-range wireless access points installed by the end-user in residential or enterprise environments. A common feature of cognitive radio and femtocells is their two-tier nature involving primary and secondary users (PUs, SUs). While this new paradigm enables innovative alternatives to conventional spectrum management and utilization, it also brings its own technical challenges. A main challenge in cognitive radio is the design of efficient resource (spectrum) trading methods. Game and microeconomics theories provide tools for studying the strategic interactions through rationality and economic benefits between PUs and SUs for effective resource allocation. In this thesis, we investigate some efficient game theoretic and microeconomic approaches to address spectrum trading in cognitive networks. We propose two auction frameworks for shared and exclusive use models. In the first auction mechanism, we consider the shared used model in cognitive radio networks and design a spectrum trading method to maximize the total satisfaction of the SUs and revenue of the Wireless Service Provider (WSP). In the second auction mechanism, we investigate spectrum trading via auction approach for exclusive usage spectrum access model in cognitive radio networks. We consider a realistic valuation function and propose an efficient concurrent Vickrey-Clarke-Grove (VCG) mechanism for non-identical channel allocation among r-minded bidders in two different cases. The realization of cognitive radio networks in practice requires the development of effective spectrum sensing methods. A fundamental question is how much time to allocate for sensing purposes. In the literature on cognitive radio, it is commonly assumed that fixed time durations are assigned for spectrum sensing and data transmission. It is however possible to improve the network performance by finding the best tradeoff between sensing time and throughput. In this thesis, we derive an expression for the total average throughput of the SUs over time-varying fading channels. Then we maximize the total average throughput in terms of sensing time and the number of SUs assigned to cooperatively sense each channel. For practical implementation, we propose a dynamical programming algorithm for joint optimization of sensing time and the number of cooperating SUs for sensing purpose. Simulation results demonstrate that significant improvement in the throughput of SUs is achieved in the case of joint optimization. In the last part of the thesis, we further address the challenge of pricing in oligopoly market for open access femtocell networks. We propose dynamic pricing schemes based on microeconomic and game theoretic approaches such as market equilibrium, Bertrand game, multiple-leader-multiple-follower Stackelberg game. Based on our approaches, the per unit price of spectrum can be determined dynamically and mobile service providers can gain more revenue than fixed pricing scheme. Our proposed methods also provide residential customers more incentives and satisfaction to participate in open access model.1 yea

Robust game-theoretic algorithms for distributed resource allocation in wireless communications

The predominant game-theoretic solutions for distributed rate-maximization algorithms in Gaussian interference channels through optimal power control require perfect channel knowledge, which is not possible in practice due to various reasons, such as estimation errors, feedback quantization and latency between channel estimation and signal transmission. This thesis therefore aims at addressing this issue through the design and analysis of robust gametheoretic algorithms for rate-maximization in Gaussian interference channels in the presence of bounded channel uncertainty. A robust rate-maximization game is formulated for the single-antenna frequency-selective Gaussian interference channel under bounded channel uncertainty. The robust-optimization equilibrium solution for this game is independent of the probability distribution of the channel uncertainty. The existence and uniqueness of the equilibrium are studied and sufficient conditions for the uniqueness of the equilibrium are provided. Distributed algorithms to compute the equilibrium solution are presented and shown to have guaranteed asymptotic convergence when the game has a unique equilibrium. The sum-rate and the price of anarchy at the equilibrium of this game are analyzed for the two-user scenario and shown to improve with increase in channel uncertainty under certain conditions. These results indicate that the robust solution moves closer to a frequency division multiple access (FDMA) solution when uncertainty increases. This leads to a higher sum-rate and a lower price of anarchy for systems where FDMA is globally optimal. A robust rate-maximization game for multi-antenna Gaussian interference channels in the presence of channel uncertainty is also developed along similar principles. It is shown that this robust game is equivalent to the nominal game with modified channel matrices. The robust-optimization equilibrium for this game and a distributed algorithm for its computation are presented and characterized. Sufficient conditions for the uniqueness of the equilibrium and asymptotic convergence of the algorithm are presented. Numerical simulations are used to confirm the behaviour of these algorithms. The analytical and numerical results of this thesis indicate that channel uncertainty is not necessarily detrimental, but can indeed result in improvement of performance of networks in particular situations, where the Nash equilibrium solution is quite inefficient and channel uncertainty leads to reduced greediness of users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Resource management for virtualized networks

Network Virtualization has emerged as a promising approach that can be employed to efficiently enhance the resource management technologies. In this work, the goal is to study how to automate the bandwidth resource management, while deploying a virtual partitioning scheme for the network bandwidth resources. Works that addressed the resource management in Virtual Networks are many, however, each has some limitations. Resource overwhelming, poor bandwidth utilization, low profits, exaggeration, and collusion are types of such sort of limitations. Indeed, the lack of adequate bandwidth allocation schemes encourages resource overwhelming, where one customer may overwhelm the resources that supposed to serve others. Static resource partitioning can resist overwhelming but at the same time it may result in poor bandwidth utilization, which means less profit rates for the Internet Service Providers (ISPs). However, deploying the technology of autonomic management can enhance the resource utilization, and maximize the customers’ satisfaction rates. It also provides the customers with a kind of privilege that should be somehow controlled as customers, always eager to maximize their payoffs, can use such a privilege to cheat. Hence, cheating actions like exaggeration and collusion can be expected. Solving the aforementioned limitations is addressed in this work. In the first part, the work deals with overcoming the problems of low profits, poor utilization, and high blocking ratios of the traditional First Ask First Allocate (FAFA) algorithm. The proposed solution is based on an Autonomic Resource Management Mechanism (ARMM). This solution deploys a smarter allocation algorithm based on the auction mechanism. At this level, to reduce the tendency of exaggeration, the Vickrey-Clarke-Groves (VCG) is proposed to provide a threat model that penalizes the exaggerating customers, based on the inconvenience they cause to others in the system. To resist the collusion, the state-dependent shadow price is calculated, based on the Markov decision theory, to represent a selling price threshold for the bandwidth units at a given state. Part two of the work solves an expanded version of the bandwidth allocation problem, but through a different methodology. In this part, the bandwidth allocation problem is expanded to a bandwidth partitioning problem. Such expansion allows dividing the link’s bandwidth resources based on the provided Quality of Service (QoS) classes, which provides better bandwidth utilization. In order to find the optimal management metrics, the problem is solved through Linear Programming (LP). A dynamic bandwidth partitioning scheme is also proposed to overcome the problems related to the static partitioning schemes, such as the poor bandwidth utilization, which can result in having under-utilized partitions. This dynamic partitioning model is deployed in a periodic manner. Periodic partitioning provides a new way to reduce the reasoning of exaggeration, when compared to the threat model, and eliminates the need of the further computational overhead. The third part of this work proposes a decentralized management scheme to solve aforementioned problems in the context of networks that are managed by Virtual Network Operators (VNOs). Such decentralization allows deploying a higher level of autonomic management, through which, the management responsibilities are distributed over the network nodes, each responsible for managing its outgoing links. Compared to the centralized schemes, such distribution provides higher reliability and easier bandwidth dimensioning. Moreover, it creates a form of two-sided competition framework that allows a double-auction environment among the network players, both customers and node controllers. Such competing environment provides a new way to reduce the exaggeration beside the periodic and threat models mentioned before. More important, it can deliver better utilization rates, lower blocking, and consequently higher profits. Finally, numerical experiments and empirical results are presented to support the proposed solutions, and to provide a comparison with other works from the literature