Anu Huttunen

Collaborative Cyclostationary Spectrum Sensing for Cognitive Radio Systems

This paper proposes an energy efficient collaborative cyclostationary spectrum sensing approach for cognitive radio systems. An existing statistical hypothesis test for the presence of cyclostationarity is extended to multiple cyclic frequencies and its asymptotic distributions are established. Collaborative test statistics are proposed for the fusion of local test statistics of the secondary users, and a censoring technique in which only informative test statistics are transmitted to the fusion center (FC) during the collaborative detection is further proposed for improving energy efficiency in mobile applications. Moreover, a technique for numerical approximation of the asymptotic distribution of the censored FC test statistic is proposed. The proposed tests are nonparametric in the sense that no assumptions on data or noise distributions are required. In addition, the tests allow dichotomizing between the desired signal and interference. Simulation experiments are provided that show the benefits of the proposed cyclostationary approach compared to energy detection, the importance of collaboration among spatially displaced secondary users for overcoming shadowing and fading effects, as well as the reliable performance of the proposed algorithms even in very low signal-to-noise ratio (SNR) regimes and under strict communication rate constraints for collaboration overhead.

Implementation of Cyclostationary Feature Detector for Cognitive Radios

Spectrum sensing is needed in cognitive radios to provide information about the surrounding radio spectrum. This enables cognitive radio system to communicate among existing radio systems without interfering them. This paper describes an FPGA implementation of a cyclostationary feature detector, which has an improved detection performance achieved by decimation of the cyclic spectrum. Decimation also provides a simple way to control detection time and, thus, allows trading the detection time to better probability of detection and vice versa. Measured detection performance is presented and detection of a 802.11g WLAN signal from air is demonstrated.

Censoring for Collaborative Spectrum Sensing in Cognitive Radios

Cooperative spectrum sensing among multiple cognitive radios mitigates the effects of shadowing and fading. However, it also generates overhead traffic which consumes more power in battery operated mobile terminals. In this paper a censoring scheme for spectrum sensing is proposed. Only informative test statistics are transmitted to the fusion center or shared with other secondary users. Two cooperative censoring test statistics based on cyclostationarity are proposed. Constant false alarm rate tests are derived and asymptotic distributions of test statistics established. The asymptotic distributions are approximated using characteristic functions. Limits for the censoring (no-send) region are derived. The performance of the proposed censoring scheme is illustrated through simulations in a multipath radio environment. Only a minor performance loss is experienced in comparison to uncensored cooperative detection even under very strict constaints on communication rates for the secondary users.

Diversity-based spectrum sensing policy for detecting primary signals over multiple frequency bands

Cognitive radios and flexible spectrum use (FSU) provide an efficient way to exploit underutilized radio spectrum by allowing secondary users to access licensed frequencies in an agile manner with the constraint that the licensed user will not be interfered. In order to identify such spectral opportunities, spectrum sensing is needed by the secondary users. In this paper a cooperative spectrum sensing policy employed by spatially displaced multiple cognitive radios is proposed. It enables sensing of multiple potentially discontinuous frequency bands simultaneously and facilitates mitigating the effects of shadowing and fading through spatial diversity.