Adaptive Game-Based Radio Spectrum Allocation in Doubly Selective Fading Channels

For cognitive radio networks, a popular approach to dynamic spectrum allocation (DSA) is game theoretic, which improves spectrum efficiency in a distributed manner. In a doubly selective fading channel, a conventional game-based DSA can be cumbersome to implement due to channel variation, which requires to re-train the channel estimator and re-calculate DSA decisions for every transmission burst within the channel coherence time. To enable DSA intelligence at affordable costs, this paper proposes novel adaptive DSA algorithms based on channel tracking. The locally predicted channels are employed to update DSA decisions, thus reducing signaling and computational overheads. Two algorithms are derived: an extended Kalman filter (EKF) assisted game (EKFG) and an EKF-updated game (EKFUG). Simulation shows that EFKUG is competitive for applications with low rate control channels by saving most communication burden and maintaining small data-rate loss, whereas EKFG is suitable for high data-rate applications that can afford moderate rate control channels.