# One Bit Spectrum Sensing in Cognitive Radio Sensor Networks

## TL;DR

This paper introduces a novel one-bit spectrum sensing algorithm for cognitive radio sensor networks, utilizing a likelihood ratio test tailored for correlated Gaussian signals, with analytical performance evaluation and simulation validation.

## Contribution

It develops a new detection criterion based on one-bit measurements for correlated Gaussian signals, extending to multiple sensors with analytical performance formulas.

## Key findings

- The proposed detector performs well with highly correlated signals.
- Analytical formulas accurately predict false alarm and detection probabilities.
- Simulation results confirm the effectiveness of the method in large sensor networks.

## Abstract

This paper proposes a spectrum sensing algorithm from one bit measurements in a cognitive radio sensor network. A likelihood ratio test (LRT) for the one bit spectrum sensing problem is derived. Different from the one bit spectrum sensing research work in the literature, the signal is assumed to be a discrete random correlated Gaussian process, where the correlation is only available within immediate successive samples of the received signal. The employed model facilitates the design of a powerful detection criteria with measurable analytical performance. One bit spectrum sensing criterion is derived for one sensor which is then generalized to multiple sensors. Performance of the detector is analyzed by obtaining closed-form formulas for the probability of false alarm and the probability of detection. Simulation results corroborate the theoretical findings and confirm the efficacy of the proposed detector in the context of highly correlated signals and large number of sensors.