  This paper proposes prediction-and-sensing based spectrum sharing, a new
spectrum-sharing model for cognitive radio networks, with a time structure for
each resource block divided into a spectrum prediction-and-sensing phase and a
data transmission phase. Cooperative spectrum prediction is incorporated as a
sub-phase of spectrum sensing in the first phase. We investigate a joint design
of transmit beamforming at the secondary base station (BS) and sensing time.
The primary design goal is to maximize the sum rate of all secondary users
(SUs) subject to the minimum rate requirement for all SUs, the transmit power
constraint at the secondary BS, and the interference power constraints at all
primary users. The original problem is difficult to solve since it is highly
nonconvex. We first convert the problem into a more tractable form, then arrive
at a convex program based on an inner approximation framework, and finally
propose a new algorithm to successively solve this convex program. We prove
that the proposed algorithm iteratively improves the objective while
guaranteeing convergence at least to local optima. Simulation results
demonstrate that the proposed algorithm reaches a stationary point after only a
few iterations with a substantial performance improvement over existing
approaches.
