Paulo Urriza

Weighted Centroid Localization Algorithm: Theoretical Analysis and Distributed Implementation

Information about primary transmitter location is crucial in enabling several key capabilities in cognitive radio networks, including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. Compared to other proposed non-interactive localization algorithms, the weighted centroid localization (WCL) scheme uses only the received signal strength information, which makes it simple to implement and robust to variations in the propagation environment. In this paper we present the first theoretical framework for WCL performance analysis in terms of its localization error distribution parameterized by node density, node placement, shadowing variance, correlation distance and inaccuracy of sensor node positioning. Using this analysis, we quantify the robustness of WCL to various physical conditions and provide design guidelines, such as node placement and spacing, for the practical deployment of WCL. We also propose a power-efficient method for implementing WCL through a distributed cluster-based algorithm, that achieves comparable accuracy with its centralized counterpart.

Optimizing Wideband Cyclostationary Spectrum Sensing Under Receiver Impairments

In the context of Cognitive Radios (CRs), cyclostationary detection of primary users (PUs) is regarded as a common method for spectrum sensing. Cyclostationary detectors rely on the knowledge of the signals symbol rate, carrier frequency, and modulation class in order to detect the present cyclic features. Cyclic frequency and sampling clock offsets are the two receiver impairments considered in this work. Cyclic frequency offsets, which occur due to oscillator frequency offsets, Doppler shifts, or imperfect knowledge of the cyclic frequencies, result in computing the test statistic at an offset from the true cyclic frequency. In this paper, we analyze the effect of cyclic frequency offsets on conventional cyclostationary detection, and propose a new multi-frame test statistic that reduces the degradation due to cyclic frequency offsets. Due to the multi-frame processing of the proposed statistic, non-coherent integration might occur across frames. Through an optimization framework developed in this work that can be performed offline, we determine the best frame length that maximizes the average detection performance of the proposed cyclostationary detection method given the statistical distributions of the receiver impairments. As a result of the optimization, the proposed detectors is shown to achieve the performance gains over conventional detectors given the constrained sensing time. We derive the proposed detectors theoretical average detection performance, and compare it to the performance of the conventional cyclostationary detector. Our analysis shows that gains in average detection performance using the proposed method can be achieved when the effect of sampling clock offset is less severe than that of the cyclic frequency offset. The analysis given in this paper can be used as a design guideline for practical implementation of cyclostationary spectrum sensors.

Multiple Antenna Cyclostationary Spectrum Sensing Based on the Cyclic Correlation Significance Test

In this paper, we propose and analyze a spectrum sensing method based on cyclostationarity specifically targeted for receivers with multiple antennas. This detection method is used for determining the presence or absence of primary users in cognitive radio networks based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. Analytical asymptotic expressions for the probability of detection and probability of false-alarm under both spatially uncorrelated and spatially correlated noise are derived and verified by simulations. The detection performance in a Rayleigh flat-fading environment is found and verified through simulations. One of the advantages of the proposed method is that the detection threshold is shown to be independent of both the number of samples and the noise covariance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown to provide higher detection probability and better robustness to noise uncertainty than existing multiple antenna cyclostationary-based spectrum sensing algorithms under both AWGN as well as a quasi-static Rayleigh fading channel.

Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions

We present a novel modulation level classification (MLC) method based on probability distribution distance functions. The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit tests. We derive the theoretical performance of the proposed MLC method and verify it via simulations. The best classification accuracy, under AWGN with SNR mismatch and phase jitter, is achieved with the proposed MLC method using Kuiper distances.

Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks

Blind modulation classification is of vital importance in spectrum surveillance applications and future heterogeneous wireless networks. In standardized wireless systems, modulation classification can be performed through exhaustive search of known signal features. Most commonly used classifiers are based on the detection of cyclostationary features, which are second-order moments of a signal, related to its carrier and symbol rate. However, when the signal parameters are unknown, an exhaustive search for cyclostationary features is energy inefficient due to high computational complexity. In this paper, we present a reconfigurable processor architecture that can blindly classify any linearly modulated signal (M-QAM, M-PSK, M-ASK, and GMSK) in addition to multi-carrier signals and spread spectrum signals. The contributions of this work are twofold. First, we analyze the complexity tradeoffs among different dependent signal processing kernels in order to minimize the total processing time and energy. Second, we optimize the processor architecture by the co-design methodology to enhance block reusability and reconfigurability. The proposed processor has been verified and synthesized in a 40-nm CMOS technology with core area of 0.06 mm2 and power dissipation of 10 mW under 0.9 V supply voltage at 500 MHz. Under a 500 MHz wide-band signal at 10 dB SNR, a complete blind classification process consumes 10.37 μJ to meet 95% of classification accuracy.

Experimental analysis of cyclostationary detectors under cyclic frequency offsets

Cyclostationary detection involves detecting cyclic features of modulated signals which are functions of various transmit parameters including the symbol rate, carrier frequency, and modulation format. However, imperfect knowledge of these transmit parameters at the sensing radio results in computing the detection test statistic under a cyclic frequency offset (CFO). The detection performance of the conventional cyclic autocorrelation function has been shown to degrade in the presence of the CFO impairment. In this paper, we propose a new multi-frame detection statistic that improves the robustness of the conventional cyclostationary detector to CFO impairments. The achievable gains in using this method over conventional detectors are quantified analytically and verified through hardware experiments using the Universal Software Radio Platform (USRP N200) transceivers.

Performance analysis of weighted centroid algorithm for primary user localization in cognitive radio networks

Information about primary user (PU) location is crucial in enabling several key capabilities in cognitive radio networks, including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. The weighted centroid localization (WCL) scheme uses only the received signal strength information, which makes it simple and robust to variations in the propagation environment. In this paper we present the first theoretical framework for WCL performance analysis in terms of its localization error distribution parameterized by node density, shadowing variance and correlation distance. Using this analysis, we quantify the robustness of WCL to various physical conditions and conclude that the performance gain by increasing node number in uncorrelated shadowing environment tends to saturate at large node density, and including more nodes in correlated shadowing environments can be harmful to the localization accuracy.

Optimal Discriminant Functions Based on Sampled Distribution Distance for Modulation Classification

In this letter, we derive the optimal discriminant functions for modulation classification based on the sampled distribution distance. The proposed method classifies various candidate constellations using a low complexity approach based on the distribution distance at specific testpoints along the cumulative distribution function. This method, based on the Bayesian decision criteria, asymptotically provides the minimum classification error possible given a set of testpoints. Testpoint locations are also optimized to improve classification performance. The method provides significant gains over existing approaches that also use the distribution distance of the signal features.

Hardware implementation of Kuiper-based modulation level classification

A modulation level classifier based on the reduced complexity Kuiper test is implemented on the BEE2 hardware prototyping platform and its performance is evaluated. In particular, the classification accuracy of the proposed classifier in distinguishing among 4, 16, and 64-QAM is analyzed under varying SNR, varying number of symbols, and different timing offsets. The performance of the proposed classifier is compared to that of the widely-used Cumulant-based classifier. Possible architectures are proposed, and their classification accuracy and computational complexity are compared. The proposed method achieves a probability of correct classification of 93% using 512 symbols at an SNR of 14dB as compared to an accuracy of 73% for the Cumulants method. The two classifiers have comparable hardware utilizations.

Eigenvalue-based cyclostationary spectrum sensing using multiple antennas

In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.