<![CDATA[ IEEE Transactions on Signal Processing - new TOC ]]>
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TOC Alert for Publication# 78 2017July 20<![CDATA[A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning]]>6518470247161005<![CDATA[Rank-One NMF-Based Initialization for NMF and Relative Error Bounds Under a Geometric Assumption]]>6518471747311691<![CDATA[Downlink Training Sequence Design for FDD Multiuser Massive MIMO Systems]]>$B$ are available at the base station (BS) to inform the users of each chosen sequence, e.g., sequences are chosen from a set of $2^B$ vectors known to the BS and users, and develop a subspace version of matching pursuit techniques to choose the desired sequences. Simulation results using realistic channel models show that the proposed solutions improve user fairness with a proper choice of weights, lead to accurate channel estimates with training durations that can be much smaller than the number of BS antennas, and show substantial gains over randomly chosen sequences for even small values of $B$.]]>6518473247441355<![CDATA[Low Complexity Moving Target Parameter Estimation for MIMO Radar Using 2D-FFT]]>651847454755968<![CDATA[Quadratic Optimization With Similarity Constraint for Unimodular Sequence Synthesis]]>$K$ blocks, and then, we sequentially optimize each block via exhaustive search while fixing the remaining $K-1$ blocks. Finally, we evaluate the computational costs and performance gains of the proposed algorithms in comparison with power method-like and semidefinite relaxation related techniques.]]>6518475647691645<![CDATA[A Stochastic Majorize-Minimize Subspace Algorithm for Online Penalized Least Squares Estimation]]>a priori or their direct computation is too intensive, and they have thus to be estimated online from the observed signals. For batch optimization of an objective function being the sum of a data fidelity term and a penalization (e.g., a sparsity promoting function), Majorize-Minimize (MM) methods have recently attracted much interest since they are fast, highly flexible, and effective in ensuring convergence. The goal of this paper is to show how these methods can be successfully extended to the case when the data fidelity term corresponds to a least squares criterion and the cost function is replaced by a sequence of stochastic approximations of it. In this context, we propose an online version of an MM subspace algorithm and we study its convergence by using suitable probabilistic tools. Simulation results illustrate the good practical performance of the proposed algorithm associated with a memory gradient subspace, when applied to both nonadaptive and adaptive filter identification problems.]]>6518477047831893<![CDATA[Boosted KZ and LLL Algorithms]]>6518478447961062<![CDATA[A Sampling Theorem for Fractional Wavelet Transform With Error Estimates]]>651847974811766<![CDATA[Adaptive Detection of a Subspace Signal in Signal-Dependent Interference]]>651848124820489<![CDATA[Bridging Mixture Model Estimation and Information Bounds Using I-MMSE]]>6518482148321297<![CDATA[Bipartite Graph Filter Banks: Polyphase Analysis and Generalization]]>IEEE Trans. Signal Process., vol. 60, no. 6, pp. 2786–2799, Jun. 2012], [“Compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs,” IEEE Trans. Signal Process., vol. 61, no. 19, pp. 4673–4685, Oct. 2013] laid the foundations for the two-channel critically sampled perfect reconstruction filter bank for signals defined on undirected graphs. This basic filter bank is applicable only to bipartite graphs but using the notion of separable filtering, the basic filter bank can be applied to any arbitrary undirected graphs. In this paper, several new theoretical results are presented. In particular, the proposed polyphase analysis yields filtering structures in the downsampled domain that are equivalent to those before downsampling and, thus, can be exploited for efficient implementation. These theoretical results also provide new insights that can be exploited in the design of these systems. These insights allow us to generalize these filter banks to directed graphs and to using a variety of graph base matrices, while also providing a link to the $\text{DSP}_G$ framework of Sandryhaila and Moura [“Discrete signal processing on graphs,” IEEE Trans. Signal Process., vol. 61, no. 7, pp. 1644–1636, Apr. 2013], [“Discrete signal processing on graphs: Frequency analysis,” IEEE Trans. Signal Process., vol. 62, no. 12, pp. 3042–3054-
Jun. 2014]. Experiments show evidence that better nonlinear approximation and denoising results may be obtained by a better selection of these base matrices.]]>6518483348461344<![CDATA[Competitive Robust Estimation for Uncertain Linear Dynamic Models]]>$H_{2}$-norm balls, it is shown that these two types of estimation problems can be recast as “semidefinite programming problems (SDPs, for short).” Numerical examples are presented for both the case of linear, finite-dimensional model classes (FIRs of a given length) and the case of nonparametric uncertain sets of causal, real-rational frequency-responses, suggesting that these two types of estimators can be attractive alternatives to the min–max MSE estimator. For the case of spectral-norm (in the finite-dimensional case) or $H_{\infty }$-norm (in the nonparametric case), the worst case MSE and approximate-regret for each candidate estimator are replaced by upper bounds obtained by Lagrangian relaxation and (somewhat conservative) versions of the estimation problems previously mentioned are posed. It is shown that these problems can also be recast as SDPs.]]>651848474861714<![CDATA[Decentralized Hypothesis Testing in Energy Harvesting Wireless Sensor Networks]]>651848624873817<![CDATA[Joint Beamforming and Power-Splitting Control in Downlink Cooperative SWIPT NOMA Systems]]>6518487448861140<![CDATA[Generalized Quadratic Matrix Programming: A Unified Framework for Linear Precoding With Arbitrary Input Distributions]]>651848874901864<![CDATA[Joint Channel and Clipping Level Estimation for OFDM in IoT-based Networks]]>6518490249111204<![CDATA[Constant Modulus Waveform Design for MIMO Radar Transmit Beampattern]]>$l_1$-norm problem, which can be solved through a double-ADMM algorithm. Finally, we assess the performance of the two proposed algorithms via numerical results.]]>6518491249231111<![CDATA[Event-Based Estimation With Information-Based Triggering and Adaptive Update]]>a priori information of the triggering mechanism of the sensor. An update mechanism with a Bayesian collapsing strategy is proposed to adaptively form state estimates at the estimator side in an unsupervised fashion. The estimator is adaptive in the sense that it is able to distinguish between having received an actual measurement or noise. The simulation results show that the proposed information-based triggering mechanism significantly outperforms its counterparts specifically in low communication rates, and confirms the effectiveness of the proposed unsupervised fusion methodology.]]>651849244939918<![CDATA[Matrix Characterization for GFDM: Low Complexity MMSE Receivers and Optimal Filters]]>6518494049552182<![CDATA[Multiple Conversions of Measurements for Nonlinear Estimation]]>6518495649701582<![CDATA[Corrections to “On Decentralized Estimation With Active Queries”]]>65184971497280<![CDATA[Corrections to “Asymptotic Achievability of the Cramér–Rao Bound for Noisy Compressive Sampling”]]>$N$ noisy measurements denoted by ${\mathbf y}$ and an overcomplete Gaussian dictionary, ${\mathbf A}$, the authors of [1] establish the existence and the asymptotic statistical efficiency of an unbiased estimator unaware of the locations of the nonzero entries, collected in set $\mathcal {I}$, in the deterministic $L$-sparse signal ${\mathbf x}$. More precisely, there exists an estimator ${\hat{\mathbf {x}}}({\mathbf y}, {\mathbf A})$ unaware of set $\mathcal {I}$ with a variance reaching the oracle-Cramér–Rao Bound in the asymptotic scenario, i.e., for $N,L\rightarrow \infty$ and $L/N \rightarrow \alpha \in (0,1)$. As was noted in the paper “Fundamental limits and constructive methods for estimation and sensing of sparse signals” by B. Babadi, the existence proof remains true even though Lemma 3.5 and (20) the paper “Asymptotic achievability of the Cramer–Rao bound for noisy compressive sampling” are inexact. In this note, the exact closed-form expression of the variance of the estimator ${\hat{\mathbf {x}}}({\mathbf y}, {\mat-
bf A})$ is provided, and its practical usefulness is numerically illustrated with the orthogonal matching pursuit estimator.]]>651849734974233