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19
Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
"... Abstract—Millimeter wave (mmWave) cellular systems will enable gigabitpersecond data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and rec ..."
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Abstract—Millimeter wave (mmWave) cellular systems will enable gigabitpersecond data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver. Due to the high cost and power consumption of gigasample mixedsignal devices, mmWave precoding will likely be divided among the analog and digital domains. The large number of antennas and the presence of analog beamforming requires the development of mmWavespecific channel estimation and precoding algorithms. This paper develops an adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel. To enable the efficient operation of this algorithm, a novel hierarchical multiresolution codebook is designed to construct training beamforming vectors
Recovering GraphStructured Activations using Adaptive Compressive Measurements
"... Abstract—We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that groups the activated vertices into few partitions, so that a topdown sensing procedure can identify these par ..."
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Cited by 9 (1 self)
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Abstract—We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements. We propose a hierarchical partitioning of the graph that groups the activated vertices into few partitions, so that a topdown sensing procedure can identify these partitions, and hence the activations, using few measurements. By exploiting the cluster structure, we are able to provide localization guarantees at weaker signal to noise ratios than in the unstructured setting. We complement this performance guarantee with an information theoretic lower bound, providing a necessary signaltonoise ratio for any algorithm to successfully localize the cluster. We verify our analysis with some simulations, demonstrating the practicality of our algorithm. I.
A Performance Guarantee for Adaptive Estimation of Sparse Signals
"... Abstract—This paper studies adaptive sensing for estimating the nonzero amplitudes of a sparse signal. We consider a previously proposed optimal twostage policy for allocating sensing resources. We derive an upper bound on the mean squared error resulting from the optimal twostage policy and a cor ..."
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Cited by 4 (2 self)
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Abstract—This paper studies adaptive sensing for estimating the nonzero amplitudes of a sparse signal. We consider a previously proposed optimal twostage policy for allocating sensing resources. We derive an upper bound on the mean squared error resulting from the optimal twostage policy and a corresponding lower bound on the improvement over nonadaptive sensing. It is shown that the adaptation gain is related to the detectability of nonzero signal components as characterized by a Bhattacharyya coefficient, thus quantifying analytically the dependence on the sparsity level of the signal, the signaltonoise ratio, and the sensing resource budget. The bound is shown to be a good approximation to the optimal twostage gain through numerical simulations. I.
Informationtheoretic bounds for adaptive sparse recovery. arXiv preprint arXiv:1402.5731
, 2014
"... Abstract—We derive an informationtheoretic lower bound for sample complexity in sparse recovery problems where inputs can be chosen sequentially and adaptively. This lower bound is in terms of a simple mutual information expression and unifies many different linear and nonlinear observation models. ..."
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Abstract—We derive an informationtheoretic lower bound for sample complexity in sparse recovery problems where inputs can be chosen sequentially and adaptively. This lower bound is in terms of a simple mutual information expression and unifies many different linear and nonlinear observation models. Using this formula we derive bounds for adaptive compressive sensing (CS), group testing and 1bit CS problems. We show that adaptivity cannot decrease sample complexity in group testing, 1bit CS and CS with linear sparsity. In contrast, we show there might be mild performance gains for CS in the sublinear regime. Our unified analysis also allows characterization of gains due to adaptivity from a wider perspective on sparse problems. I.
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
"... We develop an inference and optimal design procedure for recovering synaptic weights in neural microcircuits. We base our procedure on data from experiments in which populations of putative presynaptic neurons can be stimulated while a subthreshold recording is made from a single postsynaptic neuron ..."
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We develop an inference and optimal design procedure for recovering synaptic weights in neural microcircuits. We base our procedure on data from experiments in which populations of putative presynaptic neurons can be stimulated while a subthreshold recording is made from a single postsynaptic neuron. We present a realistic statistical model which accounts for the main sources of variability in this experiment and allows for significant prior information about the connectivity and neuronal cell types to be incorporated if available. We then present a simpler model to facilitate online experimental design via efficient variational Bayesian approximate inference methods. The optimal design approach results in equal quality posterior estimates of the synaptic weights in roughly half the number of trials under experimentally realistic simulated conditions. 1
Efficient Algorithms for Robust Onebit Compressive Sensing
"... While the conventional compressive sensing assumes measurements of infinite precision, onebit compressive sensing considers an extreme setting where each measurement is quantized to just a single bit. In this paper, we study the vector recovery problem from noisy onebit measurements, and develop ..."
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While the conventional compressive sensing assumes measurements of infinite precision, onebit compressive sensing considers an extreme setting where each measurement is quantized to just a single bit. In this paper, we study the vector recovery problem from noisy onebit measurements, and develop two novel algorithms with formal theoretical guarantees. First, we propose a passive algorithm, which is very efficient in the sense it only needs to solve a convex optimization problem that has a closedform solution. Despite the apparent simplicity, our theoretical analysis reveals that the proposed algorithm can recover both the exactly sparse and the approximately sparse vectors. In particular, for a sparse vector with s nonzero elements, the sample complexity is O(s log n/ǫ2), where n is the dimensionality and ǫ is the recovery error. This result improves significantly over the previously best known sample complexity in the noisy setting, which is O(s log n/ǫ4). Second, in the case that the noise model is known, we develop an adaptive algorithm based on the principle of active learning. The key idea is to solicit the sign information only when it cannot be inferred from the current estimator. Compared with the passive algorithm, the adaptive one has a lower sample complexity if a highprecision solution is desired.
Adaptive compressed sensing architecture in wireless braincomputer interface
 in Proceedings of the 52nd Annual Design Automation Conference. ACM
"... Wireless sensor nodes advance the braincomputer interface (BCI) from laboratory setup to practical applications. Compressed sensing (CS) theory provides a subNyquist sampling paradigm to improve the energy efficiency of electroencephalography (EEG) signal acquisition. However, EEG is a structur ..."
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Wireless sensor nodes advance the braincomputer interface (BCI) from laboratory setup to practical applications. Compressed sensing (CS) theory provides a subNyquist sampling paradigm to improve the energy efficiency of electroencephalography (EEG) signal acquisition. However, EEG is a structurevariational signal with timevarying sparsity, which decreases the efficiency of compressed sensing. In this paper, we present a new adaptive CS architecture to tackle the challenge of EEG signal acquisition. Specifically, we design a dynamic knob framework to respond to EEG signal dynamics, and then formulate its design optimization into a dynamic programming problem. We verify our proposed adaptive CS architecture on a publicly available data set. Experimental results show that our adaptive CS can improve signal reconstruction quality by more than 70 % under different energy budgets while only consuming 187.88 nJ/event. This indicates that the adaptive CS architecture can effectively adapt to the EEG signal dynamics in the BCI. 1.
Constrained adaptive sensing
"... We focus on the problem of estimating a vector x ∈ Cn from a small number of noisy linear measurements of the form yi = 〈ai,x〉+ zi, i = 1,...,m (1) where ‖ai‖2 = 1 and zi ∼ N (0, σ2). We will typically be interested in the case where the vector x is sparse, meaning that it has only s nonzeros with s ..."
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We focus on the problem of estimating a vector x ∈ Cn from a small number of noisy linear measurements of the form yi = 〈ai,x〉+ zi, i = 1,...,m (1) where ‖ai‖2 = 1 and zi ∼ N (0, σ2). We will typically be interested in the case where the vector x is sparse, meaning that it has only s nonzeros with s n. In such a case, one can obtain a significantly more accurate estimate of x by adaptively selecting the ai based on the previous measurements (compared to the standard nonadaptive approach), provided that the signaltonoise ratio (SNR) is sufficiently large (e.g., see [1–3]). In particular, a typical nonadaptive algorithm can produce an estimate x ̂ satisfying E‖x̂ − x‖22 ≤ C n logn m sσ2, (2) where C> 1 is a constant. One can show that this is essentially
A Genetic Programming Approach to CostSensitive Control in Resource Constrained Sensor Systems
"... Resource constrained sensor systems are an increasingly attractive option in a variety of environmental monitoring domains, due to continued improvements in sensor technology. However, sensors for the same measurement application can differ in terms of cost and accuracy, while fluctuations in enviro ..."
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Resource constrained sensor systems are an increasingly attractive option in a variety of environmental monitoring domains, due to continued improvements in sensor technology. However, sensors for the same measurement application can differ in terms of cost and accuracy, while fluctuations in environmental conditions can impact both application requirements and available energy. This raises the problem of automatically controlling heterogeneous sensor suites in resource constrained sensor system applications, in a manner that balances cost and accuracy of available sensors. We present a method that employs a hierarchy of model ensembles trained by genetic programming (GP): if model ensembles that poll lowcost sensors exhibit too much prediction uncertainty, they automatically transfer the burden of prediction to other GPtrained model ensembles that poll more expensive and accurate sensors. We show that, for increasingly challenging datasets, this hierarchical approach makes predictions with equivalent accuracy yet lower cost than a similar yet nonhierarchical method in which a single GPgenerated model determines which sensors to poll at any given time. Our results thus show that a hierarchy of GPtrained ensembles can serve as a control algorithm for heterogeneous sensor suites in resource constrained sensor system applications that balances cost and accuracy.