Energy-Efficient Cooperative Sensing Scheduling for Multi-Band Cognitive Radio Networks

Abstract

Abstract-In this paper, by taking both sensing performance and energy efficiency into consideration, the Cooperative Sensing Scheduling (CSS) problem for multi-band Cognitive Radio Networks (CRNs) is investigated under a practical scenario where both Primary User (PU) channels and Secondary Users (SUs) have heterogeneous characteristics. Unlike many existing works that merely claim that the CSS problem is NP-hard and then turn to heuristic methods, we analyze this problem under a solid discrete-convex framework. After formulating the CSS problem as a nonlinear binary programming problem, we adopt a threestep approach to solve it. In the first step, the number of SUs assigned to sense each PU channel is determined with the M/M ♮ -convex theory. Based on the results obtained in the first step, we then find the SU assignment using the L/L ♮ -convex theory in the second step. In the last step, the optimal number of SUs participating in sensing is obtained based on the SU assignment obtained in step two. By combining these three steps, a complete and efficient SU assignment scheme is obtained. Numerical results are provided to evaluate the performance of our proposed SU assignment scheme and validate the theoretical analysis.

Energy-Aware Cognitive Radio Systems

“…It is worthy to mention that if the circuity power is considered in this metric, the optimal EE solution can be achieved at certain values of w 1 and w 2 [4]. Similarly, a utility function that maximizes the transmission rate with the least amount of consumed energy in the sensing process is defined as [42] U rate−energy = w 1…”

“…The sensing process plays an important role in EE optimization in CR networks [7,18,24,26,29,30,33,39,41,42,46,47]. Increasing the sensing time improves the detection probability at the expense of consuming more energy, whereas reducing the sensing time results in more collisions with existing PUs due to false detection.…”

“…For a fixed frame duration, reducing the transmission time directly limits the network performance and leads to longer sensing duration, and hence, wastes energy. A single SU can sense the available spectrum for possible opportunities for its transmission [18,24,26,39,41,[46][47][48] or multiple SUs can perform the sensing in a cooperative manner in order to improve the sensing performance at the expense of spending more energy [7,29,30,33,42].…”

“…4 CR 1 can sense the PU signal and share the sensing results with CR 2 and CR 3. One of the main challenges of EE optimization in the cooperative sensing CR environment, when compared to single SU sensing strategies, is to find the optimal number and the assignment policy of SUs participating in the sensing process [7,29,30,33,42].…”

“…The most challenging part of cooperative sensing is related to the first step [29,42]. The more SUs participating in the sensing process, the higher the sensing accuracy.…”