Adaptive Energy-Efficient Spectrum Probing in Cognitive Radio Networks

In cognitive radio networks, secondary users must constantly probe the spectrum to promptly detect the arrival and the departure of primary users (PUs). However, spectrum probing is an energy-consuming process. This indicates the tradeoff between the frequency of spectrum probing and the delay of detecting the PU state change, and highlights the need for energy-conscious spectrum-probing strategies. In this paper, we provide a theoretical framework to find the optimal spectrum-probing methods that minimize the probing delay under a constraint on energy consumption in real stochastic environments. Moreover, we design a practical, sub-optimal adaptive-probing strategy that self-learns the behavior of the PU’s dynamics and exploits the proposed optimal probing method. Specifically, we find that the most widely used spectrum-probing scheme, i.e., periodic probing, is not optimal when the arrival rate of the PU state change is not constant or when the distribution of PU channel occupancy/vacancy is not uniform. On the other hand, the derived optimal and adaptive strategies can adapt to the dynamics of PUs and adjust the probing intervals based on the time-varying arrival rate of the PU state changes or the non-uniform distribution of PU channel occupancy/vacancy. Our simulation results show that the optimal spectrum-probing strategies and adaptive-probing methods perform much better and consume much less energy than periodic probing in realistic environments