[show abstract][hide abstract] ABSTRACT: Spectrum sensing is an essential functionality of cognitive radio networks (CRN). Among existing spectrum sensing methods, cooperative spectrum sensing is the best one which can achieve superior sensing performance by introducing spatial diversity of sensing data sources. Such cooperation also introduces additional information exchanging which leads to extra power consumption and reporting delay. In this paper, the optimal sensing performance problem is formulated as a nonlinear binary integer programming problem to find suitable cooperative nodes minimizing the average detection Bayesian risk. The binary particle swarm optimization (BPSO) algorithm is adopted to obtain suboptimal solutions to cooperative nodes. Computer simulations show that the proposed scheme can significantly improve the sensing performance compared with the case that all neighboring nodes participate in sensing without discrimination under different scenarios.
Proceedings of the Global Communications Conference, 2010. GLOBECOM 2010, 6-10 December 2010, Miami, Florida, USA; 01/2010
[show abstract][hide abstract] ABSTRACT: Spectrum sensing is a crucial measure for cognitive radio networks (CRN) to protect transmission of primary users. Cooperative spectrum sensing is regarded as the most promising method to improve the reliability of spectrum sensing. However, such cooperation also introduces overhead traffic of control signaling and result transmission which consumes more power in battery-operated mobile terminals. In this paper, an energy efficient transmission scheme is proposed. Clustering technique is adopted to save energy consumed in reporting results and exchanging information. All cognitive nodes are separated into a few clusters, and report local decisions to cluster heads to make cluster decisions through some data fusion method. Cluster decisions are forwarded to the common receiver to decide whether the spectrum of interest is idle or not. Simulation results demonstrate that the proposed method shows significant energy saving from 35% to 95% compared with the conventional scheme.
Wireless Communications, Networking and Mobile Computing, 2009. WiCom '09. 5th International Conference on; 10/2009
[show abstract][hide abstract] ABSTRACT: Cognitive radio technology is used to improve spectrum efficiency by having the cognitive radios act as secondary users to access primary frequency bands when they are not currently being used. In general conditions, cognitive secondary users are mobile nodes powered by battery and consuming power is one of the most important problem that facing cognitive networks; therefore, the power consumption is considered as a main constraint. In this paper, we study the performance of cognitive radio networks considering the sensing parameters as well as power constraint. The power constraint is integrated into the objective function named power efficiency which is a combination of the main system parameters of the cognitive network. We prove the existence of optimal combination of parameters such that the power efficiency is maximized. Then we reformulate the objective function to incorporate the throughput. According to different constraints or degree of significance, we may put proper weight to each term so that we could obtain more preferable combination of parameters. Computer simulations have given the optimal solution curve for different weights. We can draw the conclusion that if we put more emphasis on power efficiency, the transmit power is a more critical parameter, however if throughput is more important, the effect of sensing time is significant.
Wireless Communications and Networking Conference, 2009. WCNC 2009. IEEE; 05/2009
[show abstract][hide abstract] ABSTRACT: Spectrum sensing is an essential function for cognitive radio systems. Based on the observation that signal samples usually are correlated due to various reasons, such as oversampling, multipath propagation and correlation among raw signals, a correlation-based detection method is proposed to differentiate signals from noise in this paper. Sensing performance of the proposed method is analyzed theoretically when primary signals are fully correlated. Its complexity is compared with that of energy detection and threshold setting is discussed under an estimated false alarm probability. However, the algorithm is inferior to energy detection in sensing performance when signal samples are uncorrelated. To overcome this disadvantage, an adaptive detection model is developed. Simulations based on captured ATSC DTV signals and analog PAL TV signals are presented to verify the performance of the proposed method.
Proceedings of the 2009 ACM Workshop on Cognitive Radio Networks, CoRoNet 2009, Beijing, China, September 21, 2009; 01/2009