Spectrum sensing: A distributed approach for cognitive terminals

Abstract

Abstract-Cognitive Radios is emerging in research laboratories as a promising wireless paradigm, which will integrate benefits of software defined radio with a complete aware communication behavior. To reach this goal many issues remain still open, such as powerful algorithms for sensing the external environment. This paper presents a further step in the direction of allowing cooperative spectrum sensing in peer-to-peer cognitive networks by using distributed detection theory. The approach aims at improving the radio awareness with respect to stand alone scenario as it is shown with theoretical and experimental results.

Multi-sensor cognitive-based approach to critical infrastructure protection

“…In our example we consider that the evaluation of the required positions provided by the network of smart cameras are in the range of ± 1 meter with respect to its actual value. In order to detect the signal transmitted by the intruder the approach proposed in [21] is used. Such an approach is based on distributed detection theory and it exploits the cooperation among the cognitive radio sensor and the network of smart cameras since it needs to know the position of the radio sensors.…”

Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review

“…The optimum fusion rule for combining sensing information is the ChairVarshney rule with log-likelihood ratio test [31]. Likelihood ratio tests (LRT) are used for making classification using decision from secondary users in [29,32,33,34,35]. Different techniques for combining sensing result are employed in [12].…”

Performance Analysis and Comparative Study of Cognitive Radio Spectrum Sensing Schemes

“…These two features are fed to a Bayesian classifier for determining the active primary user and for identifying spectrum opportunities. The standard deviation of the instantaneous frequency and the maximum duration of a signal are extracted using time-frequency analysis in [22], [23], [36], [37] and neural networks are used for identification of active transmissions using these features. Cycle frequencies of the incoming signal are used for detection and signal classification in [30].…”