Robust power control in cognitive radio networks with channel uncertainty

In cognitive radio networks, channel information is desired by unlicensed secondary users (SUs) to perform effective power control so as to avoid undue interference to licensed primary users (PUs). However, in general, there is no regular information exchange between PUs and SUs, which implies that SUs are unable to obtain up-to-date channel information at the PU side. Besides, the small-scale fading, in addition to shadowing, brings great uncertainty in SUs' channel estimation. In this paper, we consider limited information exchange between SUs and PUs, and study the impact of channel uncertainty on SUs' throughput performance with power control. We model the uncertain channel gain to be a random variable following a state-dependent probability distribution function, and design a power control method that is robust against the channel uncertainty. We formulate the robust power control problem as a chance constrained robust optimization and solve it by an iterative algorithm. Numerical results show that the proposed power control can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement.