Mihir Laghate

On the effects of colluded statistical attacks in cooperative spectrum sensing

Cooperative spectrum sensing is vulnerable to attacks from malicious nodes, especially when collusion occurs. In this paper, we analyze the effect of colluded statistical attacks and show that collusion could cause performance degradation in terms of both false-alarm and detection probabilities, which is not possible via independent attacks. Closed-form expressions for system performance under the majority fusion rule are provided for a generalized form of colluded attacks. Then, for specific scenarios of collusion and mimicry attacks, we study the conditions under which the probabilities of false alarm and detection are both degraded.

Using multiple power spectrum measurements to sense signals with partial spectral overlap

In this work, we study the wideband sensing problem of detecting intermittently transmitting signals that may have partial spectral overlap. We aim to estimate the frequency bands occupied by transmitters using standards with overlapping frequency bands, such as IEEE 802.11, and those without guard bands, such as LTE-Advanced. Multiple power spectrum measurements are used to distinguish the distinct bands. An extreme ray based non-negative matrix factorization algorithm is proposed to identify measurements where a single transmitter is active. Distinct bands are identified in the presence of frequency-selective fading by using a combinatorial search. In addition, we propose a novel algorithm to automatically estimate the noise power spectrum by identifying measurements that do not have significant signal energy. Its ability to learn colored noise and LO leakage in the signal is demonstrated through USRP measurements. The dependence of the proposed algorithms performance on the medium access control protocol used by the primary users is discussed. MATLAB simulations are used to verify that the proposed algorithm detects the occupied bands more accurately than existing methods. Over the air USRP measurements in the 2.4 GHz ISM band are used to detect the occupied WiFi channels in a university environment.

Identifying the presence and footprints of multiple incumbent transmitters

Cognitive networks share spectrum with incumbent, or pre-existing, networks by identifying their presence and not causing interference to them. Current methods to identify the presence of multiple incumbent transmitters require more information than is available to a cognitive network. In this paper, the energy received by cooperating CRs is used to identify the presence of multiple incumbent transmitters without knowledge of their locations, channel model, and communication protocols. Incumbents are distinguished as components of a Gaussian mixture model of the energy received by CRs. Furthermore, the algorithm finds each incumbents footprint, i.e., the CRs that can cause the incumbent interference.

Using the Time Dimension to Sense Signals with Partial Spectral Overlap

In this work, we consider the wideband sensing problem of using a single antenna receiver for sensing an unknown number of incumbent signals occupying frequency bands that may be partially overlapping. Many communications standards today, such as the IEEE 802.11 and LTE-Advanced, use frequency bands that overlap amongst themselves or do not have a frequency guard band. Current sensing methods for a single antenna receiver sense for signal energy at individual frequencies and do not differentiate between signals. However, sensing each signal separately is necessary to make inferences about the higher layers of the incumbent communication system. Hence, in this work, signals are sensed as signal energy in a set of frequency bins instead of individual frequency bins. The number of signals and the frequency bands occupied by each are estimated. To achieve this, the temporal dimension is exploited by using periodic power measurements as input and the fact that radios transmit intermittently and not continually. Two algorithms are proposed. First, repeated non- negative matrix factorization is used to estimate the number of signals and the frequency support of each. Secondly, energy detection results on adaptively chosen frequency bins are used to detect the start and end of the frequency band occupied by each signal. Novel performance metrics are proposed since this is simultaneously a model size and parameter estimation problem. Systems with signals occupying adjacent partially overlapping frequency bands are simulated to evaluate the performance of both algorithms.

Towards instantaneous collision and interference detection using in-band full duplex

Wireless devices are ubiquitous nowadays and, since most of them use the same unlicensed frequency bands, the high number of packet losses due to interference and collisions degrade performance. Reliability, energy consumption, and latency are key challenges for future dense networks. Allowing the transmitter to take action, i.e., vacating the channel, as soon as a collision or interference is detected is crucial in improving these metrics. In-band full duplex radios enable the transmitter to simultaneously transmit packets and sense the spectrum for collisions and interference. This paper studies two important questions regarding transmitter-based collision and interference detection: (1) from an overall system perspective, does such detection outperform receiver-based detection and (2) which test statistic is the most accurate and sensitive at detecting collisions and interference. First, NS-3 simulations are used to show that transmitter-based detection reduces the energy consumption while improving the throughput in a typical star topology network. Next, we present a measurement-based study of four different techniques for transmitter-based collision and interference detection. In particular, we compare the energy detector with three goodness-of-fit tests in terms of probability of detection and false alarm. Our analysis shows that transmitter-based detection can detect between 80% to 100% of the collisions and interference occurring at the receiver, depending on the distance between the transmitter and the receiver. Of those detectable by the transmitter, our measurement results show that goodness-of-fit tests can detect nearly 100% of the collisions and have at least 10 dB better sensitivity as compared to the commonly proposed energy detection test. In general, the proposed techniques can detect interfering signals that are up to 25 dB below the remaining self-interference power.

Using belief propagation to counter correlated reports in cooperative spectrum sensing

Consider spectrum sensing systems where the presence of the primary user of the spectrum is detected by secondary users (SUs) in a centralized cooperative fashion. The sensing results can be correlated due to environmental reasons. SUs may or may not be honest. If dishonest, they could be colluding. Existing methods to fuse the SU reports either ignore the correlation in the SU reports, or they need to know the source of correlation. In this paper, we propose a belief propagation based fusion algorithm to exploit the correlations in reports of groups of SUs irrespective of cause. We show that identifying the groups of SUs having correlated reports reduces the probability of error of spectrum sensing. Our method is based on modeling the probability distribution underlying the SU reports as a Bayesian network. The process of learning the Bayesian network also shows that it is theoretically impossible to identify collusion.

Channel Access Method Classification for Cognitive Radio Applications

Motivated by improved detection and prediction of temporal holes, we propose a two stage algorithm to classify the channel access method used by a primary network. The first stage extends an existing fourth-order cumulant-based modulation classifier to distinguish between TDMA, orthogonal frequency division multiple access (OFDMA), and code division multiple access (CDMA). The second stage proposes a novel collision detector using the sample variance of the same cumulant to detect contention-based channel access methods. Our proposed method is blind and independent of the received SNR. Simulations show that our classification of TDMA, OFDMA, and CDMA is robust to network load while detection of contention outperforms existing methods.

Demonstrating spectrum sensing in colored noise for signals with partial spectral overlap

Wideband spectrum sensing aims to identify the occupied frequency bands. Conventional methods for single antenna spectrum sensors threshold the received power spectra to detect discrete frequency bins that are occupied. However, such methods neither group bins that are occupied by the same signal nor aggregate occupied bins over time to learn distinct frequency bands occupied by intermittently transmitting signals. This paper demonstrates a method to learn the frequency bands occupied by intermittently transmitting incumbent radios that occupy adjacent frequency bands without a guard band, such as by LTE-Advanced, or are spectrally overlapping, such as by IEEE 802.11. It formulates the wideband sensing problem as the factorization of a matrix consisting of multiple power spectrum measurements. A novel extreme ray based non-negative matrix factorization algorithm estimates the noise power spectrum and also determines the received power spectrum of the incumbent radios. Energy detection and a combinatorial algorithm is used to determine the unique supports of the received signals. Using a USRP N210 software defined radio as a receiver, we demonstrate that this algorithm can determine the frequency bands occupied by nearby transmitters in the 2.4GHz ISM band. Furthermore, we demonstrate that the algorithm learns the power spectrum of the colored noise experienced by the USRP.

Nearly instantaneous collision and interference detection using in-band full duplex

In wireless communication it is assumed that transmitting nodes are unable to detect collisions. However, using recent advances in in-band full duplex, a system can be developed where the self-transmitted signal is sufficiently canceled in order to detect ongoing collisions. Enabling concurrent transmission and collision detection can greatly improve wireless communication by enhancing throughput and decreasing delay and energy consumption. For this demo, we implemented a real-time collision and interference detection algorithm on the FPGA of a USRP. Inband full duplex is enabled using an electrical balance duplexer. A second USRP is used to generate interfering signals on demand.

USRP N210 demonstration of wideband sensing and blind hierarchical modulation classification

Blind modulation classification problem is particularly difficult when the exact frequency band of the signal is unknown since the modulation classifiers require accurate estimates of the signal parameters such as center frequency, bandwidth, and SNR. In this work, we demonstrate a hierarchical classification tree that filters and classifies a received signal as AM, FM, 4/16/64-QAM, 2/4/8-PAM, 4/8/16-PSK, DSSS, and FSK. Coarse estimates of signal parameters are obtained from energy detection and are refined using cyclostationary estimators. Cumulants and cyclostationarity are used to classify AM and FM while a reduced complexity Kuiper test is used for differentiating modulation level for QAM, PAM, and PSK. The effects of multipath are countered using a blind equalizer. The classifier is implemented in C++ using GNURadio libraries and is demonstrated using a USRP N210.