Conference Paper

Power allocation optimization in OFDM-based cognitive radios based on sensing information

Group of Inf. & Commun. Syst. (GSIC), Univ. de Valencia, Valencia, Spain
DOI: 10.1109/ICASSP.2011.5946700 Conference: Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Source: IEEE Xplore

ABSTRACT Owing to the non-zero probability of the missed detection and false alarm of active primary transmission, a certain degree of performance degradation of the primary user (PU) from cognitive radio users (CRs) is unavoidable. In this paper, we consider OFDM-based communication systems and present efficient algorithms to maximize the total rate of the CR by optimizing jointly both the detection operation and the power allocation, taking into account the influence of the probabilities of missed detection and false alarm, namely, the sensing accuracy. The optimization problem can be formulated as a two-variable non-convex problem, which can be solved approximately by using an alternating direction optimization method. Our algorithm can operated basically in two regimes depending on our constraints that are involved, while keeping the performance degradation of the PU bounded properly. Simulation results demonstrate that the proposed solution can considerably improve system performance.

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Available from: Baltasar Beferull-Lozano, Aug 07, 2015
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    ABSTRACT: In this paper, we propose a novel class of Nash problems for Cognitive Radio (CR) networks, modeled as Gaussian frequency-selective interference channels, wherein each secondary user (SU) competes against the others to maximize his own opportunistic throughput by choosing jointly the sensing duration, the detection thresholds, and the vector power allocation. The proposed general formulation allows to accommodate several (transmit) power and (deterministic/probabilistic) interference constraints, such as constraints on the maximum individual and/or aggregate (probabilistic) interference tolerable at the primary receivers. To keep the optimization as decentralized as possible, global (coupling) interference constraints are imposed by penalizing each SU with a set of time-varying prices based upon his contribution to the total interference; the prices are thus additional variable to optimize. The resulting players' optimization problems are nonconvex; moreover, there are possibly price clearing conditions associated with the global constraints to be satisfied by the solution. All this makes the analysis of the proposed games a challenging task; none of classical results in the game theory literature can be successfully applied. The main contribution of this paper is to develop a novel optimization-based theory for studying the proposed nonconvex games; we provide a comprehensive analysis of the existence and uniqueness of a standard Nash equilibrium, devise alternative best-response based algorithms, and establish their convergence.
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