<![CDATA[ IEEE Transactions on Signal Processing - new TOC ]]>
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TOC Alert for Publication# 78 2018February 22<![CDATA[Sketched Subspace Clustering]]>66716631675813<![CDATA[Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems]]>$ell _1$ -minimization solution by neural networks with layers representing iterations, as practiced in the learned iterative shrinkage-thresholding algorithm.]]>667167616901232<![CDATA[MISO Channel Estimation and Tracking from Received Signal Strength Feedback]]>phase retrieval ideas from optics and crystallography. Three efficient algorithms that cover different model assumptions are proposed to track the vector MISO channel on the transmitter's side using only RSS/CQI feedback. Numerical simulation results under various settings validate the efficacy of the proposed algorithms in tracking a slowly time-varying vector MISO channel. Interestingly, this is the first application of phase retrieval where assuming independent and identically distributed Gaussian measurement vectors can be practically justified.]]>667169117041239<![CDATA[Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces]]>$F$-norm distance. We theoretically prove that with high probability the affinity or distance between two projected subspaces are concentrated around their estimates. When the ambient dimension after projection is sufficiently large, the affinity and distance between two subspaces almost remain unchanged after random projection. Numerical experiments verify the theoretical work.]]>667170517201219<![CDATA[Self-Interference Cancelation Through Advanced Sampling]]>667172117331072<![CDATA[Toward Information Privacy for the Internet of Things: A Nonparametric Learning Approach]]>66717341747808<![CDATA[Error Analysis of Reconstruction From Linear Canonical Transform Based Sampling]]>667174817601215<![CDATA[Beyond Massive MIMO: The Potential of Positioning With Large Intelligent Surfaces]]>$x$ , $y$, and $z$) decreases quadratically in the surface area of the LIS. In a second step, we analyze the CRLB for positioning when there is an unknown phase $varphi$ presented in the analog circuits of the LIS. We then show that the CRLBs are dramatically degraded for all three dimensions and decrease in the third order of the surface area. Moreover, with an infinitely large LIS, the CRLB for the $z$ -dimension with an unknown $varphi$ is 6 dB higher than the case without phase uncertainty, and the CRLB for estimating $varphi$ converges to a constant that is independent of the wavelength $lambda$. At last, we extensively discuss the impact of ce-
tralized and distributed deployments of LIS, and show that a distributed deployment of LIS can enlarge the coverage and improve the overall positioning performance.]]>667176117741337<![CDATA[Fast Block LMS and RLS-Based Parameter Estimation and Two-Dimensional Imaging in Monostatic MIMO RADAR Systems With Multiple Mobile Targets]]>667177517901505<![CDATA[Adaptive Radar Detection Using Two Sets of Training Data]]>66717911801853<![CDATA[Blind Source Separation Algorithms Using Hyperbolic and Givens Rotations for High-Order QAM Constellations]]>667180218161368<![CDATA[Atomic Norm Minimization for Modal Analysis From Random and Compressed Samples]]>667181718311112<![CDATA[Low-Complexity Massive MIMO Subspace Estimation and Tracking From Low-Dimensional Projections]]>$M$ at the base-station is very large. It has been observed that in many realistic propagation scenarios, although the user channel vectors have a very high-dim $M$, they lie on low-dim subspaces because of their limited angular spread (spatial correlation). This low-dim subspace structure remains stable across several coherence blocks and can be exploited to improve the system performance. In a recent work, we addressed this problem and proposed a very effective novel algorithm referred to as Approximate Maximum-Likelihood (AML), which was formulated as a semi-definite program (SDP). In this paper, we address two problems left open in our previous work, namely, computational complexity and tracking. We propose a new algorithm that is reminiscent of Multiple Measurement Vectors (MMV) problem in Compressed Sensing and prove that it is equivalent to the AML Algorithm for sufficiently dense angular grids. It has also a very low computational complexity and is able to track the sharp transitions in the channel statistics very quickly. We provide numerical simulations to assess the estimation/tracking performance of our proposed algorithm, with a particular emphasis on situations where a direct implementation of the SDP is infeasible in real-time. Our proposed algorithm is of independent interest in applications other than massive MIMO. We provide numerical simulations to compare the performance of our algorithm with that of other related subspace estimation algorithms in the literature.]]>667183218441580<![CDATA[Accelerated Distributed Dual Averaging Over Evolving Networks of Growing Connectivity]]>667184518591416<![CDATA[Dictionary-Based Tensor Canonical Polyadic Decomposition]]>667187618891164<![CDATA[Sparse Activity Detection for Massive Connectivity]]>667189019041386<![CDATA[The Power of Side-Information in Subgraph Detection]]>66719051919782<![CDATA[Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features]]>667192019321414