Despite the lure of a considerable increase in spectrum usage efficiency, the practical implementation of cognitive radio (CR) systems is being obstructed by the need for efficient and reliable protection mechanisms that can safeguard the quality of service (QoS) requirements of licensed users. This need becomes particularly apparent in dynamic wireless networks where channel conditions may vary unpredictably – thus making the task of guaranteeing the primary users (PUs)' minimum quality of service requirements an even more challenging task. In this paper, we consider a pricing mechanism that penalizes the secondary users (SUs) for the interference they inflict on the network's PUs and then compensates the PUs accordingly. Drawing on tools from online optimization, we propose an exponential learning power allocation policy that is provably capable of adapting quickly and efficiently to the system's variability, relying only on strictly causal channel state information (CSI). If the transmission horizon T is known in advance by the SUs, we prove that the proposed algorithm reaches a " no-regret " state within O(T −1/2) iterations; otherwise, if the horizon is not known in advance, the algorithm still reaches a no-regret state within O(T −1/2 log T) iterations. Moreover, our numerical results show that the interference created by the SUs can be mitigated effectively by properly tuning the parameters of the pricing mechanism.