23 accepted papers
Q-learning is a powerful tool for network control and policy optimization in wireless networks, but it struggles with large state spaces. Recent advancements, like multi-environment mixed Q-learning (MEMQ), improves performance and reduces complexity by integrating multiple Q-learning algorithms acr…
A novel Tucker decomposition based tensor approximation is considered herein: observations based on only a few lateral slices and side structural information of a true tensor are exploited. This work is motivated by quantum chemistry problems wherein full Hessian computation is expensive, but partia…
In this paper, the causal bandit problem is investigated, in which the objective is to select an optimal sequence of interventions on nodes in a graph. By exploiting the causal relationships between the nodes whose signals contribute to the reward, interventions are optimized. First, a method to lea…
In prior work, it was shown that high performance matrix approximation/completion was possible when only a few fully sampled columns were available of a ground truth matrix if there was appropriate side information on the rowspace of the matrix. Several applications from quantum chemistry, magnetic…
In this paper, an artificial noise-aided strategy is presented for location-privacy preservation. A novel framework for the reduction of location-privacy leakage is introduced, where structured artificial noise is designed to degrade the structure of the illegitimate devices’ channel, without the ai…
A novel matrix completion problem is considered herein: observations based on fully sampled columns and quasi-polynomial side information is exploited. The framework is motivated by quantum chemistry problems wherein full matrix computation is expensive, but partial computations only lead to column…
The optimization of large-scale networks such as finding the optimal control strategies through cost minimization is challenged by large state spaces. For networks that can be modeled via Markov Decision Processes (MDP), a previously proposed graph reduction strategy is used in conjunction with a no…
Harnessing a block-sparse prior to recover signals through underdetermined linear measurements has been extensively shown to allow exact recovery in conditions where classical compressed sensing would provably fail. We exploit this result to propose a novel private communication framework where the…
Herein, an atomic norm based method for accurately estimating the location and orientation of a target from millimeter-wave multi-input-multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented. A novel virtual channel matrix is introduced and an algorithm to ex-trac…
Remote estimation over communication channels of limited capacity is an area of research with applications spanning many economically relevant areas, including cyber-physical systems and the Internet of Things. One popular choice of communication/scheduling policies used in remote estimation is the…
In this paper, improved channel gain delay estimation strategies are investigated when practical pulse shapes with finite block length and transmission bandwidth are employed. Pilot-aided channel estimation with an improved atomic norm based approach is proposed to promote the low rank structure of…
This paper formalizes a graph-constrained group testing (GT) framework for isolating up to k defective items from a population of p. In contrast to traditional group testing approaches, an underlying graphical model imposes constraints on how the items can be grouped for testing. The existing theori…
A wireless sensing system with n sensors, observing independent and identically distributed continuous random variables with a symmetric probability density function, and one non-collocated estimator acting as a fusion center is considered. The sensors transmit information to the fusion center via a…
Unimodality-constrained matrix or tensor factorization has applications in various domains, such as non-parametric source localization and data clustering, where the signals of interest are unimodal. Such factorizations are challenged by the non-convex nature of unimodality constraints. This paper d…
This paper studies the problem of localizing a source based on different types of signals measured at different sensing locations, where propagation models of the signals are not known. A tensor observation model is proposed to arrange such multimodal data into different layers to form a 3D data arr…
In this paper, target tracking constrained to short-term linear trajectories is explored. The problem is viewed as an extension of the matrix decomposition problem into low-rank and sparse components by incorporating an additional line constraint. The Cramér-Rao Bound (CRB) for the trajectory estima…
In this paper, the problem of time-varying narrowband leaked Orthogonal Frequency Division multiplexing (OFDM) channel estimation is considered. The leakage effect results from practical constraints on communication systems: finite bandwidth and block length. These practical constraints effectively…
In this paper, the estimation of a narrowband time-varying channel under the practical assumptions of finite block length and finite transmission bandwidth is investigated. It is shown that the signal after passing through a time-varying narrowband channel, under these assumptions, reveals a particu…
The estimation of a narrowband time-varying channel under finite block length and transmission bandwidth is investigated. A novel method is proposed for estimation in the delay-Doppler domain by exploiting structural constraints on low-rank matrix recovery. The proposed algorithm uses Gauss-Seidel i…
A joint radar/communication system is considered, where the radar adaptively designs the transmitted waveform such that the interference caused to the cellular systems is strictly controlled. In this paper, different Mutual Information based criteria for radar waveform optimization are proposed and…
The problem of active target detection using low rank methods is explored. In prior work, a strategy was proposed based on matrix completion for randomly sampling a field combined with binary search to localize a target. Herein, two innovations are explored: the consideration of tensor-completion in…
A problem of broad interest is the detection and localization of a target or object from its generated field. In this paper, a detection and localization strategy which exploits the structure of target fields is designed and analyzed. In taking advantage of this structure, one is able to reduce samp…
Multi-scale, multi-lag (MSML) models are adopted for time-varying (ultra)-wideband channels that are relevant for underwater acoustic, radar and ultrawideband radio applications. MSML channels are characterized by a limited number of paths, each parameterized by a delay, Doppler scale, and attenuati…