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We characterize upper bounds on the throughput capacity of a multi-tier millimeter wave (mmWave) network with diverse blockage probability, p(n), scaling scenarios. Communication links in the network are divided into three communication tiers sharing a single mmWave frequency. The bottom tier considers transmissions between n terminals and M (n) access points (APs). The top tier includes links between...
Massive MIMO-systems have received considerable attention in recent years as an enabler in future wireless communication systems. As the idea is based on having a large number of antennas at the base station it is important to have both a scalable and distributed realization of such a system to ease deployment. Most work so far have focused on the theoretical aspects although a few demonstrators have...
In prior work, the AM-FM signal model coupled with dominant component analysis has been used for fingerprint extraction for eventual fingerprint recognition. In earlier work by the authors, multirate frequency transformations were employed to transform wideband signals into narrowband signals to effect wideband AM-FM demodulation of both 1D and 2D signals. In this paper, we apply the 2D, wideband...
This work introduces an algorithm for localization of the seizure onset zone (SOZ) of epileptic patients based on electrocorticography (ECoG) recordings. The algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes, while the edge weights are the pair-wise causal influence. This causal influence is quantified by estimating the pair-wise directed...
The growing complexity of digital signal processing applications make a compelling case the use of high-level design and synthesis methodologies for the implementation on reconfigurable and embedded devices. Past research has shown that raising the level of abstraction of design stages does not necessarily gives penalties in terms of performance or resources. Dataflow programs provide behavioral descriptions...
Recovery of sparse signals with unknown clustering pattern in the case of having partial erroneous prior knowledge on the supports of the signal is considered. In this case, we provide a modified sparse Bayesian learning model to incorporate prior knowledge and simultaneously learn the unknown clustering pattern. For this purpose, we add one more layer to support-aided sparse Bayesian learning algorithm...
This paper considers the problem of reconstructing a N × N low rank positive semidefinite Toeplitz matrix from a noisy compressed sketch of size O(√r) × O (√r) where r << N is the rank of the matrix. A novel algorithm is proposed which only exploits a positive semidefinite (PSD) constraint to denoise the compressed sketch using a simple least squares approach. A major advantage of our algorithm...
This paper considers the problem of estimating the parameters of a noisy signal which has been quantized to one-bit via a time-varying thresholding operation. An expression for the Fisher information matrix (FIM) is derived for a generic deterministic signal parameterized by a vector β when the noise is independent and identically distributed (i.i.d.) Gaussian with either known or unknown variance...
We present two algorithms for fast time-domain Volterra filtering. The first algorithm computes the required products of input samples using only one multiplication per term. Since the products are explicitly computed, this algorithm can be used for adaptation as well as for filtering. The second algorithm generalizes Horner's method for polynomial evaluation and directly computes output samples without...
Previous discussions of recursive multiplication in the literature focus on how/why the scheme works and how a multiplier of a desired size can be built from given component multipliers or multiply-add modules. The form factors (square versus rectangular) for the component multipliers and the one to be synthesized, and how they affect the performance and cost of the resulting multiplier, have not...
Massive multiple-input multiple-output (MIMO) systems are one of key technologies for next generation cellular providing high spectral efficiency. While the effect of most interference and noise vanishes as the number of antennas increases, performance of massive MIMO systems is limited by pilot contamination caused by correlated pilot. Pilot reuse, allowing users in distant cells to use the same...
Stochastic computing using simple logic circuits requires significantly less area and consumes less power compared to traditional computing systems. These circuits are also inherently fault-tolerant. The main drawbacks of these systems include long latency and inexactness in computing. The deviation from exact values increases as the correlation among inputs increases. In many applications, outputs...
Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 bits/color/pixel. High dynamic range (HDR) images which are usually created from multiple exposures of the same scene, can provide 16 or 32 bits/color/pixel,...
Several combined signal processing applications such as the joint processing of EEG and MEG data can benefit from coupled tensor decompositions, for instance, the coupled CP (Canonical Polyadic) decomposition. The coupled CP decomposition jointly decomposes tensors that have at least one factor matrix in common. The SECSI (Semi-Algebraic framework for approximate CP decomposition via SImultaneaous...
Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their explicit structures are often unobservable, in order to facilitate network analytics, one generally resorts to approaches capitalizing on measurable nodal processes...
Three prominent features of massive MIMO are studied using channel measurements. Those features are extensively exploited in signal processing methods for massive MIMO and have been only partially, or not at all, validated. First, channel hardening is characterized as a function of the number of antennas. Second, user decorrelation is evaluated as a function of the distance between users. At last,...
The generalized linear model (GLM), where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform output z = Ax, arises in a range of applications such as robust regression, binary classification, quantized compressed sensing, phase retrieval, photon-limited imaging, and inference from neural spike trains. When A is large and i.i.d. Gaussian, the generalized...
The recently developed super-resolution framework by Candes enables direction-of-arrival (DOA) estimation from a sparse spatial power spectrum in the continuous domain with infinite precision in the noise-free case. By means of atomic norm minimization (ANM), the discretization of the spatial domain is no longer required, which overcomes the basis mismatch problem in conventional sparse signal recovery...
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