Energy minimization approach for optimal cooperative spectrum sensing in sensor-aided cognitive radio networks

Abstract

In a sensor-aided cognitive radio network, collaborating battery-powered sensors are deployed to aid the network in cooperative spectrum sensing. These sensors consume energy for spectrum sensing and therefore deplete their life-time, thus we study the key issue in minimizing the sensing energy consumed by such group of collaborating sensors. The IEEE P802.22 standard specifies spectrum sensing accuracy by the detection and false alarm probabilities, hence we address the energy minimization problem under this detection accuracy constraint. Firstly, we derive the bounds for the number of sensors to simultaneously guarantee the thresholds for high detection probability and low false alarm probability. With these bounds, we then formulate the optimization problem to find the optimal sensing interval and the optimal number of sensor that minimize the energy consumption. Thirdly, the approximated analytical solutions are derived to solve the optimization accurately and efficiently in polynomial time. Finally, numerical results show that the minimized energy is significantly lower than the energy consumed by a group of randomly selected sensors. The mean absolute error of the approximated optimal sensing interval compared with the exact value is less than 4% and 8% under good and bad SNR conditions, respectively. The approximated optimal number of sensors is shown to be very close to the exact number.

Radar, TV and Cellular Bands: Which Spectrum Access Techniques for Which Bands?

“…This extra overhead will be particularly high if soft combining techniques are employed or in scenarios with a large number of short-range PUs with high mobility and unpredictable behavior. Node selection and censoring are mentioned in the literature as possible techniques to increase the energy efficiency and minimize the traffic on the CCC in CRNs without significantly affecting CSS performance [28], [34], [35]. By using node selection, some CRs are allowed to enter sleep mode, which leads to lower sensing and transmission costs for the overall network.…”

On Results’ Reporting of Cooperative Spectrum Sensing in Cognitive Radio Networks

“…Such approaches include most of the proposed energy efficient CSS proposed in the literature, such as cluster-based CSS [22,23], dynamic-head cluster-based CSS [35], confidence-voting scheme [22], censoring scheme [25], minimizing the number of participating CU in CSS [15,16,18], and optimizing the sensing time [18] or the fusion rule [19]. In this section, we prove the consistency of the proposed approach with only the energy-efficient cluster-based CSS approach as an example.…”

“…In [17], a CU will participates in CSS only if its expected energy consumption is less than a threshold. The optimizing of the sensing time is exploited to reduce energy consumption in [18]. Optimizing the FR for maximizing energy efficiency is addressed under different setups and constraints in [19][20][21].…”

On the Energy Consumption of the Decision-Fusion Rules in Cognitive Radio Networks

“…Therefore, a major and open research issue is to asses the energy consumption of cooperative spectrum sensing with these realistic assumptions, in order to understand their actual benefits. Various approaches for energy efficient cooperative spectrum sensing has been reported in [10], [11] and [12], to which the interested reader is referred for further information.…”