Subspace based low rank and joint sparse matrix recovery

We consider the recovery of a low rank and jointly sparse matrix from under sampled measurements of its columns. This problem is highly relevant in the recovery of dynamic MRI data with high spatio-temporal resolution, where each column of the matrix corresponds to a frame in the image time series; the matrix is highly low-rank since the frames are highly correlated. Similarly the non-zero locations of the matrix in appropriate transform/frame domains (e.g. wavelet, gradient) are roughly the same in different frame. The superset of the support can be safely assumed to be jointly sparse. Unlike the classical multiple measurement vector (MMV) setup that measures all the snapshots using the same matrix, we consider each snapshot to be measured using a different measurement matrix. We show that this approach reduces the total number of measurements, especially when the rank of the matrix is much smaller than than its sparsity. Our experiments in the context of dynamic imaging shows that this approach is very useful in realizing free breathing cardiac MRI.Comment: 5 pages, 5 figures, Asilomar 2014 conference submissio

PHY-layer link quality indicators for wireless networks using matched-filters

We present a novel approach to accurate real-time estimation of wireless link quality using simple matched-filtering techniques. Our approach is based on the simple observation that there is a portion of each packet transmission from any given node that does not change from one packet to another; this includes preamble sequences used to synchronize the receiver and also address information in the packet header used for medium access control and routing. Our approach can be thought of as a generalized and simplified variant of standard signal processing techniques that are commonly used for preamble detection, automatic gain control, carrier sensing and other functions in many packet wireless networks. By using a combination of energy detection and correlation techniques, we show that we can effectively detect packet transmissions in real-time with low complexity, without decoding the packets themselves, and indeed, even without detailed knowledge of the packet format. We present extensive experimental results from a software-defined radio testbed to illustrate the effectiveness of this approach for 802.15.4 (Zigbee) networks even in the presence of strong interference signals and low SNR.Comment: 6 page

Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks

Deep-learning based classification algorithms have been shown to be susceptible to adversarial attacks: minor changes to the input of classifiers can dramatically change their outputs, while being imperceptible to humans. In this paper, we present a simple hypothesis about a feature compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations. Drawing on ideas from information and coding theory, we propose a general class of defenses for detecting classifier errors caused by abnormally small input perturbations. We further show theoretical guarantees for the performance of this detection method. We present experimental results with (a) a voice recognition system, and (b) a digit recognition system using the MNIST database, to demonstrate the effectiveness of the proposed defense methods. The ideas in this paper are motivated by a simple analogy between AI classifiers and the standard Shannon model of a communication system.Comment: 44 Pages, 2 Theorems, 35 Figures, 29 Tables. arXiv admin note: substantial text overlap with arXiv:1901.0941

Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees

We introduce a two step algorithm with theoretical guarantees to recover a jointly sparse and low-rank matrix from undersampled measurements of its columns. The algorithm first estimates the row subspace of the matrix using a set of common measurements of the columns. In the second step, the subspace aware recovery of the matrix is solved using a simple least square algorithm. The results are verified in the context of recovering CINE data from undersampled measurements; we obtain good recovery when the sampling conditions are satisfied.Comment: 4 pages, 4 figures, ISBI 2015 conference submissio

from the U.S. Army

Distributed transmit beamforming is a form of cooperative communication in which two or more information sources simultaneously transmit a common message and control the phase of their transmissions so that the signals constructively combine at an intended destination. Depending on the design objectives and constraints, the power gains of distributed beamforming can be translated into dramatic increases in range, rate, or energy efficiency. Distributed beamforming may also provide benefits in terms of security and interference reduction since less transmit power is scattered in unintended directions. Key challenges in realizing these benefits, however, include coordinating the sources for information sharing and timing synchronization and, most crucially, distributed carrier synchronization so that the transmissions combine constructively at the destination. This article reviews promising recent results in architectures, algorithms, and working prototypes which indicate that these challenges can be surmounted. Directions for future research needed to translate the potential of distributed beamforming into practice are also discussed

Distributed Coordination with Deaf Neighbors: Efficient Medium Access for 60 GHz Mesh Networks

Abstract—Multi-gigabit outdoor mesh networks operating in the unlicensed 60 GHz “millimeter (mm) wave ” band, offer the possibility of a quickly deployable broadband extension of the Internet. We consider mesh nodes with electronically steerable antenna arrays, with both the transmitter and receiver synthesizing narrow beams that compensate for the higher path loss at mm-wave frequencies, achieving ranges on the order of 100 meters using the relatively low transmit powers attainable with low-cost silicon implementations. Such highly directional networking differs from WiFi networks at lower carrier frequencies in two ways that have a crucial impact on protocol design: (1) directionality drastically reduces spatial interference, so that pseudowired link abstractions form an excellent basis for protocol design; (2) directionality induces deafness, which makes medium access control (MAC) based on carrier sensing infeasible. Interference analysis in our prior work shows that, in such a setting, coordination between transmitters and receivers, rather than interference management, becomes the key MAC performance bottleneck. However, the question of whether such coordination can be achieved in a distributed fashion while achieving high medium utilization, was left open. In this paper, we answer this question in the affirmative, presenting a distributed MAC protocol that employs memory to achieve approximate time division multiplexed (TDM) schedules without explicit coordination or resource allocation. The efficacy of the protocol is demonstrated via packet level simulations, while a Markov chain fixed-point analysis provides insight into the effect of parameter choices

A qos framework for stabilized collision channels with multiuser detection

Abstract — Recent work has shown that cross-layer optimization of the physical layer and Medium Access Control for a wireless collision channel, based on a receiver with adaptive multiuser detection capability, is capable of providing significantly better performance than classical Aloha. The basic features of such a system are multipacket reception (MPR) capability, and the ability (with high probability) to estimate the number of contending users even when the packets are not successfully received. We provide an analytical model that includes these features, and use it to derive methods for backlog estimation and stabilization. Two classes of users are considered: high priority users with Quality of Service (QoS) requirements, who must succeed within a deadline with a specified probability; and low priority users whose throughput we wish to maximize, while maintaining the QoS for high priority users, and keeping the overall system stable. We obtain contention policies that ensure QoS and stability, based on backlog estimates obtained by extending Rivest’s pseudo-Bayesian technique for classical Aloha. The channel throughput and the achievable QoS is characterized as a function of the arrival rates for high and low priority users. Finally, we apply these methods to simulations of a system employing Differential Minimum Mean Squared Error (DMMSE) adaptive multiuser detection, and find that the analytical model provides accurate guidelines for design and performance predictions. I

1 Interference analysis for highly directional 60 GHz mesh networks: the case for rethinking medium

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Multi-Antenna Interference Cancellation Techniques for Cognitive Radio Applications

Abstract — This paper presents a practical method for using multi-antenna radios to cancel interference in cognitive radio systems. Under this method, secondary radio transmitters use beamforming techniques to find antenna weights that place nulls at the primary receivers, and secondary radio receivers use adaptive techniques to decode in the presence of interference from primary users. As an example, we show how this scheme can be leveraged to effectively reuse the uplink band of a cellular network. However, estimating the channel responses, without causing interference and without requiring significant modifications to legacy systems, is a challenging problem. We provide an iterative method for accurate channel estimation in frequency division duplexed networks, where the uplink is independent of the downlink. I