We study the channel assignment strategy in multichannel wireless sensor networks (WSNs) where macrocells and sensor nodes are overlaid. The WSNs dynamically access the licensed spectrum owned by the macrocells to provide pervasive sensing services. We formulate the channel assignment problem as a potential game which has at least one pure strategy Nash equilibrium (NE). To achieve the NE, we propose a stochastic learning-based algorithm which does not require the information of other players' actions and the time-varying channel. Cluster heads as players in the game act as self-organized learning automata and adjust assignment strategies based on their own action-reward history. The convergence property of the proposed algorithm toward pure strategy NE points is shown theoretically and verified numerically. Simulation results demonstrate that the learning algorithm yields a 26% sensor node capacity improvement as compared to the random selection, and incurs less than 10% capacity loss compared to the exhaustive search.

In this paper, we propose a user scheduling scheme which is based merely on the ergodic rate. We derive the ergodic rate of each user for our network MIMO system from the average Signal-to-Noise Ratio (SNR) and the covariance of prediction error. Further more, we design a joint precoding for the selected users. The precoding matrices are contrived according to the predicted Channel State Information at Transmitter (CSIT) in the future time slot to reduce the deviation caused by delay. Such a network MIMO system is under consideration, where the users are cooperatively served by multiple Base Stations (BSs). User scheduling and joint precoding are executed by a central unit connected to all BSs. Both the above functions require the CSIT which is acquired by an uplink feedback overhead. Simulation results demonstrate a conspicuous improvement in user spectral efficiency with reduced feedback.

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Abstract-The spectrum sharing has recently passed into a mainstream Cognitive Radio (CR) strategy. We investigate the core issue in this strategy: interference mitigation at Primary Receiver (PR). We propose a linear precoder design which aims at alleviating the interference caused by Secondary User (SU) from the source for Orthogonal Space-Time Block Coding (OSTBC) based CR. We resort to Minimum Variance (MV) approach to contrive the precoding matrix at Secondary Transmitter (ST) in order to maximize the Signal to Noise Ratio (SNR) at Secondary Receiver (SR) on the premise that the orthogonality of OSTBC is kept, the interference introduced to Primary Link (PL) by Secondary Link (SL) is maintained under a tolerable level and the total transmitted power constraint at ST is satisfied. Moreover, the selection of polarization mode for SL is incorporated in the precoder design. In order to provide an analytic solution with low computational cost, we put forward an original precoder design algorithm which exploits an auxiliary variable to treat the optimization problem with a mixture of linear and quadratic constraints. Numerical results demonstrate that our proposed precoder design enable SR to have an agreeable SNR on the prerequisite that the interference at PR is maintained below the threshold.Index Terms-Cognitive radio, precoder design, orthogonal space-time block coding, polarized antennas.