arXiv subjects

Antonia Tulino

Publications and source records attributed to Antonia Tulino.

12 recordsLinked to original sources

arXiv2024

Nuclear Atomic Norm for parametric estimation of sparse channels

Parametric channel estimation in mmWave not only enables the anticipated large spectral efficiency gains of \acs{MIMO} systems but also reveals important propagation parameters, allowing for a low complexity representation of the channel matrix. In this work, we propose to use atomic norm as a gridless multidimensional spectral estimation approach to address parametric channel estimation where both AoD and AoA are identified. The conditions for recovery of the propagation parameters are given depending on properties of the measurement matrix, and on structural features such as the antenna geometry or the number of scatters to resolve. The proposed methodology is compared against several state-of-the-art parametric approaches.

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arXiv2020

Gridless Multidimensional Angle of Arrival Estimation for Arbitrary 3D Antenna Arrays

A full multi--dimensional characterization of the angle of arrival (AoA) has immediate applications to the efficient operation of modern wireless communication systems. In this work, we develop a compressed sensing based method to extract multi-dimensional AoA information exploiting the sparse nature of the signal received by a sensor array. The proposed solution, based on the atomic $\ell_0$ norm, enables accurate gridless resolution of the AoA in systems with arbitrary 3D antenna arrays. Our approach allows characterizing the maximum number of distinct sources (or scatters) that can be identified for a given number of antennas and array geometry. Both noiseless and noisy measurement scenarios are addressed, deriving and evaluating the resolvability of the AoA propagation parameters through a multi--level Toeplitz matrix rank--minimization problem. To facilitate the implementation of the proposed solution, we also present a least squares approach regularized by a convex relaxation of the rank-minimization problem and characterize its conditions for resolvability.

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arXiv2020

Rényi Entropy Bounds on the Active Learning Cost-Performance Tradeoff

Semi-supervised classification, one of the most prominent fields in machine learning, studies how to combine the statistical knowledge of the often abundant unlabeled data with the often limited labeled data in order to maximize overall classification accuracy. In this context, the process of actively choosing the data to be labeled is referred to as active learning. In this paper, we initiate the non-asymptotic analysis of the optimal policy for semi-supervised classification with actively obtained labeled data. Considering a general Bayesian classification model, we provide the first characterization of the jointly optimal active learning and semi-supervised classification policy, in terms of the cost-performance tradeoff driven by the label query budget (number of data items to be labeled) and overall classification accuracy. Leveraging recent results on the Rényi Entropy, we derive tight information-theoretic bounds on such active learning cost-performance tradeoff.

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arXiv2019

Optimal Control of Distributed Computing Networks with Mixed-Cast Traffic Flows

Distributed computing networks, tasked with both packet transmission and processing, require the joint optimization of communication and computation resources. We develop a dynamic control policy that determines both routes and processing locations for packets upon their arrival at a distributed computing network. The proposed policy, referred to as Universal Computing Network Control (UCNC), guarantees that packets i) are processed by a specified chain of service functions, ii) follow cycle-free routes between consecutive functions, and iii) are delivered to their corresponding set of destinations via proper packet duplications. UCNC is shown to be throughput-optimal for any mix of unicast and multicast traffic, and is the first throughput-optimal policy for non-unicast traffic in distributed computing networks with both communication and computation constraints. Moreover, simulation results suggest that UCNC yields substantially lower average packet delay compared with existing control policies for unicast traffic.

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arXiv2019

Fundamental Limits of Erasure-Coded Key-Value Stores with Side Information

In applications of distributed storage systems to modern key-value stores, the stored data is highly dynamic due to frequent updates. The multi-version coding problem was formulated to study the cost of storing dynamic data in distributed storage systems. Previous work on multi-version coding considered a completely decentralized and asynchronous system assuming that the servers are not aware of which versions of the data are received by the other servers. In this paper, we relax this assumption and study a system where a server may acquire side information of the data versions propagated to some other servers based on the network topology. Specifically, we study a storage system with $n$ servers over a directed graph that store $ν$ totally ordered versions of a message. Each server receives a subset of these $ν$ versions. A server is aware of which versions have been received by its neighbors in the network graph. We show that the side information can result in a better storage cost as compared with the case where there is no side information for some regimes at the expense of the additional latency associated with exchanging the side information. Through an information-theoretic converse, we identify surprising scenarios where the side information may not help in improving the worst-case storage cost beyond the case where servers have no side information. Finally, we present a case study over Amazon web services (AWS) that demonstrates the potential cost reductions that may be obtained by our constructions.

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arXiv2017

Truncated Polynomial Expansion Downlink Precoders and Uplink Detectors for Massive MIMO

In TDD reciprocity-based massive MIMO it is essential to be able to compute the downlink precoding matrix over all OFDM resource blocks within a small fraction of the uplink-downlink slot duration. Early implementation of massive MIMO are limited to the simple Conjugate Beamforming (ConjBF) precoding method, because of such computation latency limitation. However, it has been widely demonstrated by theoretical analysis and system simulation that Regularized Zero-Forcing (RZF) precoding is generally much more effective than ConjBF for a large but practical number of transmit antennas. In order to recover a significant fraction of the gap between ConjBF and RZF and yet meeting the very strict computation latency constraints, truncated polynomial expansion (TPE) methods have been proposed. In this paper we present a novel TPE method that outperforms all previously proposed methods in the general non-symmetric case of users with arbitrary antenna correlation. In addition, the proposed method is significantly simpler and more flexible than previously proposed methods based on deterministic equivalents and free probability in large random matrix theory. We consider power allocation with our TPE approach, and show that classical system optimization problems such as min-sum power and max-min rate can be easily solved. Furthermore, we provide a detailed computation latency analysis specifically targeted to a highly parallel FPGA hardware architecture.

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arXiv2017

Rate-Memory Trade-off for the Two-User Broadcast Caching Network with Correlated Sources

This paper studies the fundamental limits of caching in a network with two receivers and two files generated by a two-component discrete memoryless source with arbitrary joint distribution. Each receiver is equipped with a cache of equal capacity, and the requested files are delivered over a shared error-free broadcast link. First, a lower bound on the optimal peak rate-memory trade-off is provided. Then, in order to leverage the correlation among the library files to alleviate the load over the shared link, a two-step correlation-aware cache-aided coded multicast (CACM) scheme is proposed. The first step uses Gray-Wyner source coding to represent the library via one common and two private descriptions, such that a second correlation-unaware multiple-request CACM step can exploit the additional coded multicast opportunities that arise. It is shown that the rate achieved by the proposed two-step scheme matches the lower bound for a significant memory regime and it is within half of the conditional entropy for all other memory values.

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arXiv2016

Cache-Aided Coded Multicast for Correlated Sources

The combination of edge caching and coded multicasting is a promising approach to improve the efficiency of content delivery over cache-aided networks. The global caching gain resulting from content overlap distributed across the network in current solutions is limited due to the increasingly personalized nature of the content consumed by users. In this paper, the cache-aided coded multicast problem is generalized to account for the correlation among the network content by formulating a source compression problem with distributed side information. A correlation-aware achievable scheme is proposed and an upper bound on its performance is derived. It is shown that considerable load reductions can be achieved, compared to state of the art correlation-unaware schemes, when caching and delivery phases specifically account for the correlation among the content files.

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arXiv2016

Correlation-Aware Distributed Caching and Coded Delivery

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple groupcast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly unpredictable and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored. This paper generalizes the cache-aided coded multicast problem to a source compression with distributed side information problem that specifically accounts for the correlation among the content files. It is shown how joint file compression during the caching and delivery phases can provide load reductions that go beyond those achieved with existing schemes. This is accomplished through a lower bound on the fundamental rate-memory trade-off as well as a correlation-aware achievable scheme, shown to significantly outperform state-of-the-art correlation-unaware solutions, while approaching the limiting rate-memory trade-off.

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arXiv2016

Achievable Rates of Multidimensional Rotationally Invariant Distributions

The maximum achievable rate or mutual informa- tion of multidimensional rotationally invariant distributions in the presence of additive white Gaussian noise is analyzed. A simple expression for the special case of multisphere distributions is derived. Such distributions consist of points in a multidimensional Euclidean space that are uniformly distributed over several multidimensional concentric hyperspheres. For the 2-dimensional case, such distributions have been previously considered in order to reduce the computational complexity of finding a bound on the channel capacity of fiber-optic channels. These distributions take advantage of the statistical rotational invariance of the noise and nonlinear distortions in fiber-optic channels. Using the derived mutual information expression, 2- and 4-dimensional multisphere distributions are compared for fiber-optic dual-polarization channels dominated by linear noise. At high signal-to-noise ratios, 4-dimensional multisphere distributions offer higher achievable rates than 2-dimensional ones transmitted on each of the two polarizations of the optical carrier. Such 4-dimensional multisphere distributions are also shown to be statistically invariant under 4-dimensional nonlinear transmission in fibers.

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arXiv2015

Caching-Aided Coded Multicasting with Multiple Random Requests

The capacity of caching networks has received considerable attention in the past few years. A particularly studied setting is the shared link caching network, in which a single source with access to a file library communicates with multiple users, each having the capability to store segments (packets) of the library files, over a shared multicast link. Each user requests one file from the library according to a common demand distribution and the server sends a coded multicast message to satisfy all users at once. The problem consists of finding the smallest possible average codeword length to satisfy such requests. In this paper, we consider the generalization to the case where each user places L >= 1 independent requests according to the same common demand distribution. We propose an achievable scheme based on random vector (packetized) caching placement and multiple groupcast index coding, shown to be order-optimal in the asymptotic regime in which the number of packets per file B goes to infinity. We then show that the scalar (B = 1) version of the proposed scheme can still preserve order-optimality when the number of per-user requests L is large enough. Our results provide the first order-optimal characterization of the shared link caching network with multiple random requests, revealing the key effects of L on the performance of caching-aided coded multicast schemes.

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arXiv2012

Support Recovery with Sparsely Sampled Free Random Matrices

Consider a Bernoulli-Gaussian complex $n$-vector whose components are $V_i = X_i B_i$, with $X_i \sim \Cc\Nc(0,\Pc_x)$ and binary $B_i$ mutually independent and iid across $i$. This random $q$-sparse vector is multiplied by a square random matrix $\Um$, and a randomly chosen subset, of average size $n p$, $p \in [0,1]$, of the resulting vector components is then observed in additive Gaussian noise. We extend the scope of conventional noisy compressive sampling models where $\Um$ is typically %A16 the identity or a matrix with iid components, to allow $\Um$ satisfying a certain freeness condition. This class of matrices encompasses Haar matrices and other unitarily invariant matrices. We use the replica method and the decoupling principle of Guo and Verdú, as well as a number of information theoretic bounds, to study the input-output mutual information and the support recovery error rate in the limit of $n \to \infty$. We also extend the scope of the large deviation approach of Rangan, Fletcher and Goyal and characterize the performance of a class of estimators encompassing thresholded linear MMSE and $\ell_1$ relaxation.

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