Person: Chen, Hsieh-Chung
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Hsieh-Chung
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Chen, Hsieh-Chung
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Publication Wireless Inference-based Notification (WIN) without Packet Decoding(USENIX Association, 2013) Chen, Hsieh-Chung; Kung, H.We consider ultra-energy-efficient wireless transmission of notifications in sensor networks. We argue that the usual practice where a receiver decodes packets sent by a remote node to acquire its state or message is suboptimal in energy use. We propose an alternative approach where a receiver first (1) performs physical-layer matched filtering on arrived packets without actually decoding them at the link layer or higher layer, and then (2) based on the matching results infers the sender's state or message from the time-series pattern of packet arrivals. We show that hierarchical multi-layer inference can be effective for this purpose in coping with channel noise. Because packets are not required to be decodable by the receiver, the sender can reach a farther receiver without increasing the transmit power or, equivalently, a receiver at the same distance with a lower transmit power. We call our scheme Wireless Inference-based Notification (WIN) without Packet Decoding. We demonstrate by analysis and simulation that WIN allows a sender to multiply its notification distance. We show how senders can realize these energy-efficiency benefits with unchanged system and protocols; only receivers, which normally are larger systems than senders and have ample computing and power resources for WIN-related processing.Publication Gradient Descent for Optimization Problems With Sparse Solutions(2016-05-18) Chen, Hsieh-Chung; Kung, H.T.; Gortler, Steven; Li, NaSparse modeling is central to many machine learning and signal processing algorithms, because finding a parsimonious model often implicitly removes noise and reveals structure in data. They appear in applications such as feature selection, feature extraction, sparse support vector machines, sparse logistic regression, denoising, and compressive sensing. This raises a great interest in solving optimization problems with sparse solutions. There has been substantial interest in sparse optimization in the last two decades. Out of the various approaches, the gradient descent methods and the path following methods have been most successful. Existing path following methods are mostly designed for specific problems. Gradient descent methods are more general, but they do not explicitly leverage the fact that the solution is sparse. This thesis develops the auxiliary sparse homotopy (ASH) method for gradient de- scent, which is designed to converge quickly to answers with few non-zero components by maintaining sparse interim state while making sufficient descent. ASH modifies gradient methods by applying an auxiliary sparsity constraint that relaxes dynamically overtime. This principle is applicable to general gradient descent methods, including accelerated proximal gradient descent, coordinate descent, and stochastic gradient descent. For sparse optimization problems, ASH modified algorithms converge faster than the unmodified counterparts, while inheriting their convergence guarantees and flexibility in handling various regularization functions. We demonstrate the advantages of ASH in several applications. Even though some of these problems (notably LASSO) have attracted many dedicated solvers over the years, we find that ASH is very competitive against the state-of-the-art for all these applications in terms of convergence speed and cost per-iteration.Publication Compressed Statistical Testing and Application to Radar(2012-12-06) Chen, Hsieh-Chung; Kung, H.; Wicks, Michael C.We present compressed statistical testing (CST) with an illustrative application to radar target detection. We characterize an optimality condition for a compressed domain test to yield the same result as the corresponding test in the uncompressed domain. We demonstrate by simulation that under high SNR, a likelihood ratio test with compressed samples at 3.3x or even higher compression ratio can achieve detection performance comparable to that with uncompressed data. For example, our compressed domain Sample Matrix Inversion test for radar target detection can achieve constant false alarm rate (CFAR) performance similar to the corresponding test in the raw data domain. By exploiting signal sparsity in the target and interference returns, compressive sensing based CST can incur a much lower processing cost in statistical training and decision making, and can therefore enable a variety of distributed applications such as target detection on resource limited mobile devices.Publication Collaborative Compressive Spectrum Sensing in a UAV Environment(Institute of Electrical and Electronics Engineers, 2011) Chen, Hsieh-Chung; Kung, H.; Vlah, Dario; Hague, Daniel; Muccio, Michael; Poland, BrendonSpectrum sensing is of fundamental importance to many wireless applications including cognitive radio channel assignment and radiolocation. However, conventional spectrum sensing can be prohibitively expensive in computation and network bandwidth when the bands under scanning are wide and highly contested. In this paper we propose distributed spectrum sensing with multiple sensing nodes in a UAV environment. The ground nodes in our scheme sense the spectrum in parallel using compressive sensing. Each sensor node transmits compressive measurements to a nearby UAV in the air. The UAV performs decoding on the received measurements; it decodes information with increasing resolution as it receives more measurements. Furthermore, by a property of compressive sensing decoding, frequencies of large magnitude responses are recovered first. In the proposed scheme, as soon as the UAV detects the presence of such high-power frequencies from a sensor, this information is used to aid decoding for other sensors. We argue that such collaboration enabled by UAV will greatly enhance the decoding accuracy of compressive sensing. We use packet-loss traces acquired in UAV flight experiments in the field, as well as field experiments involving software-defined radios, to validate the effectiveness of this distributed compressive sensing approach.Publication Measurement Combining and Progressive Reconstruction in Compressive Sensing(Institute of Electrical and Electronics Engineers, 2011) Chen, Hsieh-Chung; Kung, H.; Vlah, Dario; Suter, BruceCompressive sensing has emerged as an important new technique in signal acquisition due to the surprising property that a sparse signal can be captured from measurements obtained at a sub-Nyquist rate. The decoding cost of compressive sensing, however, grows superlinearly with the problem size. In distributed sensor systems, the aggregate amount of compressive measurements encoded by the sensors can be substantial, and the decode cost for all the variables involved can be large. In this paper we propose a method to combine measurements from distributed sensors. With our method we can transport and store a single combined measurement set, rather than multiple sets for all sensors. We show that via source separation and joint decoding, it is possible to recover an approximate to the original signal from combined measurements using progressive reconstruction which focuses on individual sensors. This results in a reduction in the number of variables used in decoding and consequently a reduced decoding time. We show that the computed approximation to the signal can still have sufficient accuracy for target detection. We describe the combining approach and the associated progressive reconstruction, and we illustrate them with image recovery for simple target detection examples.Publication Separation-Based Joint Decoding in Compressive Sensing(Institute of Electrical and Electronics Engineers, 2011) Chen, Hsieh-Chung; Kung, H.We introduce a joint decoding method for compressive sensing that can simultaneously exploit sparsity of individual components of a composite signal. Our method can significantly reduce the total number of variables decoded jointly by separating variables of large magnitudes in one domain and using only these variables to represent the domain. Furthermore, we enhance the separation accuracy by using joint decoding across multiple domains iteratively. This separation-based approach improves the decoding time and quality of the recovered signal. We demonstrate these benefits analytically and by presenting empirical results.