In this work, we propose a novel algorithm for adaptive demodulation in impulse noise channels. The proposed method computes appropriate log-likelihood ratios (LLR) based on four previously established parameter estimation techniques of Symmetric α-Stable noise. The methods are evaluated with Monte Carlo simulations and shown to outperform the compared demodulators. Simulations show that the methods perform close to the theoretical limit in different proportions of impulse noise and in pure, randomized α-Stable interference. The proposed adaptive demodulation technique can be used to improve decoding or demodulation performance in many real-world situations where non-Gaussian interference commonly occurs.

In this work, a novel out-of-band interference cancellation (OOBIC) method, based on a full duplex interference cancellation technique, is proposed. The aim of the OOBIC is to counteract the interference that may occur when collocated radios transmit and receive in closely located frequency bands. The main advantage of the method is that the cancellation can be performed directly on the data signal, with the desired signal present, without complex nonlinear modeling, and without additional synchronization between the collocated radios. The method is validated both analytically and through simulations, and is shown to successfully increase the signal quality in OOB interference situations where the signal otherwise would have been unrecoverable.

Electromagnetic interference can cause severe performance degradation on wireless communication systems. The performance degradation is largely affected by the characteristics of the interference. With knowledge of the actual interference environment, radio receivers can be adapted to interference characteristics, and thereby providing greatly improved performance. In this work, we propose two different methods for adaptive demodulation in impulse noise. The proposed methods compute appropriate log- likelihood ratios (LLR) based on classification and parameter estimation, respectively, of Middleton's Class A noise. The methods are evaluated with Monte Carlo simulations and shown to outperform the compared demodulators. Simulations show that the methods perform close to the theoretical limit in different proportions of impulse noise and in pure, randomized Class A interference. The proposed adaptive demodulation techniques can be used to improve decoding or demodulation performance in many real-life situations where non-Gaussian interference commonly occurs.

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

In this work, we consider spectrum sensing of Gaussian signals with structured covariance matrices. We show that the optimal detector based on the probability distribution of the sample covariance matrix is equivalent to the optimal detector based on the raw data, if the covariance matrices are known. However, the covariance matrices are unknown in general. Therefore, we propose to estimate the unknown parameters using covariance matching estimation techniques (COMET). We also derive the optimal detector based on a Gaussian approximation of the sample covariance matrix, and show that this is closely connected to COMET.

In this paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigen-value multiplicities. We derive the generalized likelihood-ratio test (GLRT) for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio and show that the GLRT for these applications, of which some are already known, are special cases of the general result.

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 Neyman-Pearson 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.

We point out an error in a derivation in the recent paper, and provide a correct and much shorter calculation of the result in question. In passing, we also connect the results in to the literature on array signal processing and on principal component analysis, and show that the signal detection methods proposed in follow as special cases of standard results in these fields.

We consider detection of signals encoded with orthogonal space-time 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.

In this paper, we consider spectrum sensing of DVB-T signals in different fading environments. We compare state-of-the-art detectors including detectors based on pilot subcarriers, as well as detectors for general OFDM signals that exploit the correlation structure incurred by the cyclic prefix. Energy detection is also included for comparison. We show numerically that the choice of detector depends on the scenario, the detector requirements, and on the available prior knowledge. We also show that it is possible to obtain good detection performance by exploiting the correlation, even in a frequency selective channel.

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.