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One of the more commonly envisioned algorithms for cognitive radios is spectrum filling via dynamic frequency selection. Applying the cognitive radio design framework proposed in [1i, weformalize a low complexity distributed ad-hoc dynamic frequency sele ction algorithm that converges to near-minimal interference frequency re-use patterns. We then examine the performance of this algorithm in the presences of practical considerations such as intra-network policy variations and timing issues and show that while this leads to situations that violate the framework of [1i, the steady-state and convergence properties oftheframework are stillpreserved INTRODUCTION1When deployed in a network, the adaptations of cognitive radios yield an interactive decision problem which several authors have proposed modeling with game theory. By leveraging the potential game model, we proposed in [1] a framework-the interference reducing network (IRN)for cognitive radio design that ensures the selfish adaptations of interacting cognitive radios converge to a low interference state. In brief, the framework requires each adaptation made by a cognitive radio to reduce the sum network interference. While it is easy to satisfy this condition with networks that employ centralized decision processes or elaborate observation sharing processes, this paper proposes a distributed and autonomous dynamic frequency selection algorithm (DFS) suitable for use in 802.1 1h that satisfies the IRN framework without cooperation between access nodes.

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We show that the fixed power, synchronous Interference Avoidance (IA) scheme of [3] employing the (greedy) eigen-iteration can be modeled as the recently developed potential game of [10]. Motivated by the fact that receivers can make small mistakes, we consider the convergence of the eigeniteration when noise is added in a manner similar to [2]. Further, we restrict ourselves to a class of signal environments that we call levelable environments. Applying game-theory, we obtain a convergence result similar to that of [2] for levelable environments: arbitrarily small noise assures that the eigeniteration almost surely converges to a neighborhood of the optimum signature set.

When cognitive radios operate in a network, each II. RELATED WORK link's adaptations impact the decisions of other cognitive radios which spawns an interactive decision processes. The existence of Paste work o ngwavef a atn algipth ansmfocuse Fo these interactive processes could potentially limit the deployment system with a single rasm s of cognitive radios as it is difficult to guarantee that the resulting the uplink of a synchronous CDMA system with a single basebehavior will avoid a tragedy of the commons, much less provide station, [3] and [4] propose an algorithm where the system optimal performance. This paper proposes a novel design updates the signature sequence, Sk, of each user, k, in a roundframework that ensures that cognitive radio interactions are robin fashion where each update is intended to improve the beneficial and reduce sum network interference with each SINR of user k at the base-station which is implementing a adaptation. Five different approaches to implementing algorithms Minimum Mean Square Error (MMSE) receiver. Specifically, that satisfy this framework are presented -two of which rely on given signature sequence Sk(n) at iteration n the updated collaboration and three which permit autonomous adaptations. signature sequence is given by (1)

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