Energy-Efficient Power Loading for OFDM-Based Cognitive Radio Systems With Channel Uncertainties

In this paper, we propose a novel algorithm to optimize the energy efficiency (EE) of orthogonal frequency-division multiplexing (OFDM)-based cognitive radio systems under channel uncertainties. We formulate an optimization problem that guarantees a minimum required rate and a specified power budget for the secondary user (SU) while restricting the interference to primary users (PUs) in a statistical manner. The optimization problem is nonconvex, and it is transformed to an equivalent problem using the concept of fractional programming. Unlike all related works in the literature, we consider the effect of imperfect channel-state information (CSI) on the links between the SU transmitter and receiver pairs, and we additionally consider the effect of limited sensing capabilities of the SU. Since the interference constraints are met statistically, the SU transmitter does not require perfect CSI feedback from the PUs' receivers. Simulation results show that the EE deteriorates as the channel estimation error increases. Comparisons with relevant works from the literature show that the interference thresholds at the PUs' receivers can be severely exceeded and that the EE is slightly deteriorated if the SU does not account for spectrum sensing errors.