Optimal Spectrum Sensing Interval in MISO Cognitive Small Cell Networks

This paper considers a cognitive small cell network, where one cognitive base station (CBS) transmits information to the cognitive user and energy to the energy harvesting receivers (EHRs). The Markov channel model is exploited to characterize the state change of the macrocell base station. The spectrum sensing time, the spectrum sensing interval, and the beamforming matrixes of the CBS are jointly optimized to achieve three goals: the maximization of the CBS throughput, the minimization of the energy cost of the CBS, and the minimization of the interferences to the macrocell users (MUEs). These objectives are optimized subject to the interference constraints of the MUEs, the secrecy rate constraint, the transmit power constraint of the CBS, and the energy harvesting constraints of the EHRs. The formulated problems are challenging non-convex and difficult to solve. A 1-D line search method and semidefinite relaxation-based algorithm is proposed to solve these problems. It is proved that the optimal solution can be obtained under some conditions. If the conditions are not satisfied, Gaussian randomization procedure is used to obtain the suboptimal solutions. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme.