Anastasia Lavrenko

Self-organized common control channel design for Cognitive Radio Ad Hoc Networks

Cognitive Radio Ad Hoc Network (CRAHN) is an emergent paradigm in wireless communications that brings the promise of mitigating the well-known scalability problems of classical Mobile Ad Hoc Networks (MANETs). By integrating in its architecture novel dynamic spectrum access algorithms, the CRAHN is expected to increase usage efficiency of available spectrum resources. However, the performance of such algorithms, distributed and cooperative in its nature, is tightly coupled with the existence of a signaling layer, or common control channel (CCC), that is established between neighboring Cognitive Radios (CRs). In this paper we propose a Distributed Consensus Algorithm to address the problem of distributed CCC allocation in CRAHNs. The proposed solution defines a token ring architecture for the CCC and introduces an additional feature, the token-embedded pilot-tone, used by the consensus algorithm to derive a channel quality metric. Through a simulation study of the Distributed Consensus Algorithm, we evaluate its performance for different network scenarios; the results show a significant increase of network capacity and spectrum efficiency when compared to a sequence-based rendezvous scheme.

Compressive energy detection for blind coarse wideband sensing: Comparative performance study

Wideband signal acquisition and spectrum sensing play a crucial role in a number of applications. In this work we discuss the task of blind spectrum sensing of frequency-sparse wideband signals sampled at sub-Nyquist rates. We show how in a generic sub-Nyquist sampling framework the results of the support recovery can be directly used for coarse multichannel energy detection. We numerically study the performance of the proposed compressive energy detector and compare it with that of the related approaches. Our results demonstrate that it outperforms its closest counterpart that operates on the recovered power spectral density and provides a comparable performance to the Nyquist-rate energy detector in the high SNRs.

Sparsity order estimation for single snapshot compressed sensing

In this paper we discuss the estimation of the spar-sity order for a Compressed Sensing scenario where only a single snapshot is available. We demonstrate that a specific design of the sensing matrix based on Khatri-Rao products enables us to transform this problem into the estimation of a matrix rank in the presence of additive noise. Thereby, we can apply existing model order selection algorithms to determine the sparsity order. The matrix is a rearranged version of the observation vector which can be constructed by concatenating a series of non-overlapping or overlapping blocks of the original observation vector. In both cases, a Khatri-Rao structured measurement matrix is required with the main difference that in the latter case, one of the factors must be a Vandermonde matrix. We discuss the choice of the parameters and show that an increasing amount of block overlap improves the sparsity order estimation but it increases the coherence of the sensing matrix. We also explain briefly that the proposed measurement matrix design introduces certain multilinear structures into the observations which enables us to apply tensor-based signal processing, e.g., for enhanced denoising or improved sparsity order estimation.

Spatially resolved sub-Nyquist sensing of multiband signals with arbitrary antenna arrays

In recent years it has been shown that wideband analog signals can be sampled significantly below the Nyquist rate without loss of information, provided that the unknown frequency support occupies only a small fraction of the overall bandwidth. The modulated wideband converter (MWC) is a particular architecture that implements this idea. In this paper we discuss how the use of antenna arrays allows to extend this concept towards spatially resolved wideband spectrum sensing by leveraging the sparsity in the angular-frequency domain. In our system each antenna element of the array is sampled at a sub-Nyquist rate by an individual MWC block. This results in a trade-off between the number of antennas and MWC channels per antenna. We derive bounds on the minimal total number of channels and minimal sampling rate required for perfect recovery of the 2D angular-frequency spectrum of the incoming signal and present a concrete reconstruction approach. The proposed system is applicable to arbitrary antenna arrays, provided that the array manifold is ambiguity-free.

Design and analysis of compressive antenna arrays for direction of arrival estimation

The design of compressive antenna arrays for direction of arrival (DOA) estimation has been investigated. The main aim is to provide a larger aperture with a reduced hardware complexity.The basic receiver architecture of such a compressive array is presented and a generic system model that includes different options for the hardware implementation is introduced.The design of the analog combining network that performs the receiver channel reduction is discussed and two design approaches are proposed.A comparison to sparse arrays and compressive arrays with randomly chosen combining kernels is presented, showing the superiority of the proposed designs. In this paper we investigate the design of compressive antenna arrays for narrow-band direction of arrival (DOA) estimation that aim to provide a larger aperture with a reduced hardware complexity and allowing reconfigurability, by a linear combination of the antenna outputs to a lower number of receiver channels. We present a basic receiver architecture of such a compressive array and introduce a generic system model that includes different options for the hardware implementation. We then discuss the design of the analog combining network that performs the receiver channel reduction, and propose two design approaches. The first approach is based on the spatial correlation function which is a low-complexity scheme that in certain cases admits a closed-form solution. The second approach is based on minimizing the Cramr-Rao Bound (CRB) with the constraint to limit the probability of false detection of paths to a pre-specified level. Our numerical simulations demonstrate the superiority of the proposed optimized compressive arrays compared to the sparse arrays of the same complexity and to compressive arrays with randomly chosen combining kernels.

On the SNR Variability in Noisy Compressed Sensing

Compressed sensing (CS) is a sampling paradigm that allows us to simultaneously measure and compress signals that are sparse or compressible in some domain. The choice of a sensing matrix that carries out the measurement has a defining impact on the system performance and it is often advocated to draw its elements randomly. It has been noted that in the presence of input (signal) noise, the application of the sensing matrix causes signal-to-noise ratio (SNR) degradation due to the noise folding effect. In fact, it might also result in the variations of the output SNR in compressive measurements over the support of the input signal, potentially resulting in unexpected nonuniform system performance. In this letter, we study the impact of a distribution from which the elements of a sensing matrix are drawn on the spread of the output SNR. We derive analytic expressions for several common types of sensing matrices and show that the SNR spread grows with the decrease of the number of measurements. This makes its negative effect especially pronounced for high compression rates that are often of interest in CS.

On the sensing matrix performance for support recovery of noisy sparse signals

Recovery of sparse signals from few linear measurements is a central task of the recently emerged area of compressed sensing. Evidently, the design of the measurement plays a key role in the signal recoverability. In this contribution we analyze the explicit dependence between a deterministic sensing matrix and the support recovery performance. We do so by deriving the probability of wrong support recovery and output SNR in the presence of additive input noise. Due to tractability, a closed-form analytical expression can only be found for the 1-sparse case. However, we present numerical evidence that the expressions obtained for 1-sparse case qualitatively capture the trend for the more general iV-sparse case as well. Additionally, the investigations reveal that when designing a measurement, along with the low coherence one has to ensure a stable output SNR. We provide an example of a sensing matrix that, despite having slightly higher coherence, is superior compared to the conventional random matrix with i.i.d. Gaussian entries in terms of the support recovery performance due to providing a constant output SNR.

Cognitive Scout Node for Communication in Disaster Scenarios

The cognitive radio (CR) concept has appeared as a promising technology to cope with the spectrum scarcity caused by increased spectrum demand due to the emergence of new applications. CR can be an appropriate mean to establish self-organization and situation awareness at the radio interface, which is highly desired to manage unexpected situations that may happen in a disaster scenario. The scout node proposed in this paper is an extended concept based on a powerful CR node in a heterogeneous nodes environment which takes a leading role for highly flexible, fast, and robust establishment of cooperative wireless links in a disaster situation. This node should have two components: one is a passive sensor unit that collects and stores the technical knowledge about the electromagnetic environment in a data processing unit so-called “radio environment map” in the form of a dynamically updated database, and other is an active transceiver unit which can automatically be configured either as a secondary node for opportunistic communication or as a cooperative base station or access point for primary network in emergency communications. Scout solution can be viable by taking advantage of the technologies used by existing radio surveillance systems in the context of CR.

Interference detection in centralized cooperative spectrum sensing from sub-Nyquist samples

Wideband spectrum sensing plays a crucial role in a number of applications among which the cognitive radio (CR) is one of the most prominent. In this work we consider a scenario where the wide band of interest is comprised of multiple communication channels occupied by several independent transmissions. Due to the propagation conditions, some of the transmissions can cause potential interference by occupying the same channel at different locations. In order to alleviate the effect of such interference, we employ a network of distributed sensing nodes that sample the wideband signal at sub-Nyquist rate and share the acquired data with the fusion center. We show that using the structure of the correlations between the sub-Nyquist samples obtained at different sensors, we can detect the presence of potential in-channel interference. We present a concrete approach for estimation of the central frequencies of the channels in which it occurs and demonstrate its effectiveness in simulations.

On the earth mover's distance as a performance metric for sparse support recovery

Compressed Sensing (CS) is a recently emerged framework for simultaneous sampling and compression of signals that are sparse or compressible in some representation. Besides signal reconstruction, the CS framework is often adopted for compressive parameter estimation. Performance metrics commonly used in CS are well suited for performance evaluation in terms of recovery rates but provide little insight into the estimation accuracy in a parameter estimation setting. In this contribution, we study an alternative metric based on the Earth Movers Distance (EMD). We define the EMD in the context of support recovery and derive exact formulas for its calculation for supports with equal as well as arbitrary cardinalities. Our simulation results suggest that the EMD provides a better alternative to common CS metrics in that it reflects the distance between the individual estimates in case of the imperfect support recovery.