SNR-Walls in Eigenvalue-based Spectrum Sensing

Various spectrum sensing approaches have been shown to suffer from a so-called SNR-wall, an SNR value below which a detector cannot perform robustly no matter how many observations are used. Up to now, the eigenvalue-based maximum-minimum-eigenvalue (MME) detector has been a notable exception. For instance, the model uncertainty of imperfect knowledge of the receiver noise power, which is known to be responsible for the energy detector's fundamental limits, does not adversely affect the MME detector's performance. While additive white Gaussian noise (AWGN) is a standard assumption in wireless communications, it is not a reasonable one for the MME detector. In fact, in this work we prove that uncertainty in the amount of noise coloring does lead to an SNR-wall for the MME detector. We derive a lower bound on this SNR-wall and evaluate it for example scenarios. The findings are supported by numerical simulations.Comment: 17 pages, 3 figures, submitted to EURASIP Journal on Wireless Communications and Networkin

Compressive cyclostationary spectrum sensing with a constant false alarm rate

Spectrum sensing is a crucial component of opportunistic spectrum access schemes, which aim at improving spectrum utilization by allowing for the reuse of idle licensed spectrum. Sensing a spectral band before using it makes sure the legitimate users are not disturbed. To that end, a number of different spectrum sensing method have been developed in the literature. Cyclostationary detection is a particular sensing approach that takes use of the built-in periodicities characteristic to most man-made signals. It offers a compromise between achievable performance and the amount of prior information needed. However, it often requires a significant amount of data in order to provide a reliable estimate of the cyclic autocorrelation (CA) function. In this work, we take advantage of the inherent sparsity of the cyclic spectrum in order to estimate CA from a low number of linear measurements and enable blind cyclostationary spectrum sensing. Particularly, we propose two compressive spectrum sensing algorithms that exploit further prior information on the CA structure. In the first one, we make use of the joint sparsity of the CA vectors with regard to the time delay, while in the second one, we introduce structure dictionary to enhance the reconstruction performance. Furthermore, we extend a statistical test for cyclostationarity to accommodate sparse cyclic spectra. Our numerical results demonstrate that the new methods achieve a near constant false alarm rate behavior in contrast to earlier approaches from the literature

Morc3 silences endogenous retroviruses by enabling Daxx-mediated histone H3.3 incorporation

Endogenous retroviruses (ERVs) comprise a significant portion of mammalian genomes. Although specific ERV loci feature regulatory roles for host gene expression, most ERV integrations are transcriptionally repressed by Setdb1-mediated H3K9me3 and DNA methylation. However, the protein network which regulates the deposition of these chromatin modifications is still incompletely understood. Here, we perform a genome-wide single guide RNA (sgRNA) screen for genes involved in ERV silencing and identify the GHKL ATPase protein Morc3 as a top-scoring hit. Morc3 knock-out (ko) cells display de-repression, reduced H3K9me3, and increased chromatin accessibility of distinct ERV families. We find that the Morc3 ATPase cycle and Morc3 SUMOylation are important for ERV chromatin regulation. Proteomic analyses reveal that Morc3 mutant proteins fail to interact with the histone H3.3 chaperone Daxx. This interaction depends on Morc3 SUMOylation and Daxx SUMO binding. Notably, in Morc3 ko cells, we observe strongly reduced histone H3.3 on Morc3 binding sites. Thus, our data demonstrate Morc3 as a critical regulator of Daxx-mediated histone H3.3 incorporation to ERV regions

Spectrum sensing in cognitive radio

Reliable spectrum sensing is the main enabler for opportunistic access to the underutilized wireless spectrum. The task of a spectrum sensing algorithm is to decide between two hypotheses, the one that the spectral band under observation is free and can be used by a secondary system (H0), and the one that the primary system is transmitting on the band, such that the secondary system needs to refrain from accessing it (H1). The goal in the design of spectrum sensing algorithms is to maximize the probability of detecting a present primary system transmission (probability of detection Pd) given a fixed probability of wrongly determining the band under observation to be occupied when it is not (probability of false alarm Pfa ). In the case of a missed detection, i. e., when the primary system is transmitting but the spectrum sensing algorithm decides that the band is free, the secondary system might also start a transmission, by which it will disturb the primary system. When a false alarm happens, the secondary system misses a chance to use the spectrum. In this thesis, contributions have been made to three types of spectrum sensing algorithms. The first type of spectrum sensing we consider is cyclostationarity detection. Cyclostationarity is a stochastic feature present in all man-made signals, e.g., wireless communication signals, but is absent in pure stationary noise. Due to this property it can be used to decide between H0 and H1, which makes it a good fit for spectrum sensing. The problem arising is that in order to determine the presence or absence of cyclostationarity in a received signal, it has to be known beforehand which cycle frequency is affected. In blind spectrum sensing it is assumed that the secondary system possesses no knowledge about the primary system signal, which, for the above reasons, rules out the use of cyclostationarity. Based on methods from the field of compressed sensing, two algorithms for tackling this problem are proposed. In a second step, a modification of a classic test for cyclostationarity is devised to estimate the test statistic. This modification is necessary to work around the problem that when using the compressed sensing cyclic autocorrelation estimation algorithms, information required for estimating the spectrum sensing test statistic is lost. Furthermore, to assess the cyclic autocorrelation estimation performance of the aforementioned algorithms, a closed-form expression of the discrete-time cyclic autocorrelation of linearly modulated signals with a rectangular pulse shape is derived. Eigenvalue-based spectrum sensing builds on the idea that a communication signal induces either correlation in time or correlation between different receivers, while pure i.i.d. noise does not. The eigenvalues of a received signal’s covariance matrix are used to define various test statistics for spectrum sensing. One of these is the condition number used in the maximum-minimum-eigenvalue (MME) detector. The MME detector is independent of uncertainty regarding the receiver noise power. In contrast, this uncertainty has been shown to lead to an SNR-wall in the energy detector. An SNR-wall constitutes the SNR-value that separates the regime where a detector can robustly detect a primary system signal and the regime where it cannot. Obviously, not exhibiting an SNR-wall is a desired feature of spectrum sensing algorithms. Unfortunately, the MME detector does not possess this feature. Indeed, in this work we show that the MME detector suffers from an SNR-wall induced by uncertainty regarding the amount of coloring of the receiver noise. A lower bound on this SNR-wall is derived and examples for different types of covariance matrices are given. Moreover, it is shown that low amounts of man-made impulsive noise already lead to enough uncertainty in the noise coloring that an SNR-wall considerably far above the desired regime of operation is brought about. Furthermore, two new test statistics for spectrum sensing based on the eigenvalues of the received signal’s covariance matrix are proposed. One of the oldest test statistics used in spectrum sensing is the received signal power. The corresponding method goes by the name of energy detection. It consists of measuring the received energy in a spectral band and comparing it to a predefined threshold. One of the problems occurring in spectrum sensing is the so-called hidden terminal problem, which leads to an SNR between the active node of the primary system and the secondary system sensor that is too low for reliable detection. In order to avoid the problem, a set of spatially distributed sensors is deployed. To exploit the spatial diversity, the sensors have to transmit either a local decision on the spectrum occupancy or their measurement data to a fusion center for combined analysis and decision making. To minimize the resulting overhead in spectrum usage, compressed sensing methods are utilized. Finally, the architecture of the simulation framework used for most numerical evaluations presented in this work is described. It facilitates the reuse of code and benefits its stability