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Abstract-We consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely known noise and signal powers, we set up a vector-matrix model for an OFDM signal with a cyclic prefix and derive the optimal NeymanPearson detector from first principles. The optimal detector exploits the inherent correlation of the OFDM signal incurred by the repetition of data in the cyclic prefix, using knowledge of the length of the cyclic prefix and the length of the OFDM symbol. We compare the optimal detector to the energy detector numerically. We show that the energy detector is near-optimal (within 1 dB SNR) when the noise variance is known. Thus, when the noise power is known, no substantial gain can be achieved by using any other detector than the energy detector.For the case of completely unknown noise and signal powers, we derive a generalized likelihood ratio test (GLRT) based on empirical second-order statistics of the received data. The proposed GLRT detector exploits the non-stationary correlation structure of the OFDM signal and does not require any knowledge of the noise power or the signal power. The GLRT detector is compared to state-of-the-art OFDM signal detectors, and shown to improve the detection performance with 5 dB SNR in relevant cases.

Abstract-We present a survey of state-of-the-art algorithms for spectrum sensing in cognitive radio. The algorithms discussed range from energy detection to sophisticated feature detectors. The feature detectors that we present all have in common that they exploit some known structure of the transmitted signal. In particular we treat detectors that exploit cyclostationarity properties of the signal, and detectors that exploit a known eigenvalue structure of the signal covariance matrix. We also consider cooperative detection. Specifically we present data fusion rules for soft and hard combining, and discuss the energy efficiency of several different sensing, sleeping and censoring schemes in detail.

We consider detection of signals encoded with orthogonal spacetime block codes (OSTBC), using multiple receive antennas. Such signals contain redundancy and they have a specific structure, that can be exploited for detection. We derive the optimal detector, in the Neyman-Pearson sense, when all parameters are known. We also consider unknown noise variance, signal variance and channel coefficients. We propose a number of GLRT based detectors for the different cases, that exploit the redundancy structure of the OSTBC signal. We also propose an eigenvalue-based detector for the case when all parameters are unknown. The proposed detectors are compared to the energy detector. We show that when only the noise variance is known, there is no gain in exploiting the structure of the OSTBC. However, when the noise variance is unknown there can be a significant gain.

This paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. The focus is on the case when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. We illustrate the results in the context of the latter.

In this paper we deal with spoofing detection in GNSS receivers. We derive the optimal genie detector when the true positions are per fectly known, and the observation errors are Gaussian, as a bench mark for other detectors. The system model considers three dimen sional positions, and includes correlated errors. In addition, we pro pose several detectors that do not need any position knowledge, that outperform recently proposed detectors in many interesting cases.

ABSTRACT:We evaluate the detection performance of several commercial interference detectors and of a detector that uses the automatic gain control (AGC) level as a test statistic. The evaluations are based on actual measurements of GPS signals and different types of jamming signals, and were performed at the Vidsel test range in northern Sweden. The AGC detector and the Chronos CTL-3500 were shown to work well for all types of jamming signals. The J-alert was able to detect a wideband (20 MHz) signal but not the narrow band (<2 MHz) signals. By contrast, the jamming indicator on a Ublox 6H receiver was only able to detect a slowly varying modulated CW signal, but not signals with larger bandwidth (>2 MHz). We confirmed that C/N 0 -based Android application detectors could work well in static scenarios but are not suitable in dynamic scenarios, since they cannot distinguish between decreased GPS signal strength and increased interference.

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