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4. Cell Attention Networks

Author

Giusti, Lorenzo, primary, Battiloro, Claudio, additional, Testa, Lucia, additional, Di Lorenzo, Paolo, additional, Sardellitti, Stefania, additional, and Barbarossa, Sergio, additional

Published
2023
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9. Reconfigurable Intelligent Surface Aided Mobile Edge Computing over Intermittent mmWave Links

Author

Airod, Fatima Ezzahra, Merluzzi, Mattia, Di Lorenzo, Paolo, and Strinati, Emilio Calvanese

Subjects
Computer Science - Information Theory, Computer Science - Emerging Technologies
Abstract

The advent of Reconfigurable Intelligent Surfaces (RISs) in wireless communication networks unlocks the way to support high frequency radio access (e.g. in millimeter wave) while overcoming their sensitivity to the presence of deep fading and blockages. In support of this vision, this work exhibits the forward-looking perception of using RIS to enhance the connectivity of the communication links in edge computing scenarios, to support computation offloading services. We consider a multi-user MIMO system, and we formulate a long-term optimization problem aiming to ensure a bounded end-to-end delay with the minimum users average transmit power, by jointly selecting uplink user precoding, RIS reflectivity parameters, and computation resources at a mobile edge host. Thanks to the marriage of Lyapunov stochastic optimization, projected gradient techniques and convex optimization, the problem is efficiently solved in a per-slot basis, requiring only the observation of instantaneous realizations of time-varying radio channels and task arrivals, and that of communication and computing buffers. Numerical simulations show the effectiveness of our method and the benefits of the RIS, in striking the best trade-off between power consumption and delay for different blocking conditions, also when different levels of channel knowledge are assumed.

Published
2022

17. Wireless Environment as a Service Enabled by Reconfigurable Intelligent Surfaces: The RISE-6G Perspective

Author

Strinati, Emilio Calvanese, primary, Alexandropoulos, George C., additional, Sciancalepore, Vincenzo, additional, Di Renzo, Marco, additional, Wymeersch, Henk, additional, Phan-Huy, Dinh-Thuy, additional, Crozzoli, Maurizio, additional, D'Errico, Raffaele, additional, De Carvalho, Elisabeth, additional, Popovski, Petar, additional, Di Lorenzo, Paolo, additional, Bastianelli, Luca, additional, Belouar, Mathieu, additional, Mascolo, Julien Etienne, additional, Gradoni, Gabriele, additional, Phang, Sendy, additional, Lerosey, Geoffroy, additional, and Denis, Benoit, additional

Published
2021
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39. Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies.

Author

Di Lorenzo, Paolo, Banelli, Paolo, Isufi, Elvin, Barbarossa, Sergio, and Leus, Geert

Subjects
*ADAPTIVE signal processing, *GRAPH theory, *SAMPLING methods, *COMPUTER algorithms, *TIME-varying systems, *STOCHASTIC convergence
Abstract

The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed mean-square analysis illustrates the effect of random sampling on the adaptive reconstruction capability and the steady-state performance. Then, several probabilistic sampling strategies are proposed to design the sampling probability at each node in the graph, with the aim of optimizing the tradeoff between steady-state performance, graph sampling rate, and convergence rate of the adaptive algorithms. Finally, a distributed RLS strategy is derived and shown to be convergent to its centralized counterpart. Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and recovery strategies for (distributed) adaptive learning of signals defined over graphs. [ABSTRACT FROM AUTHOR]

Published
2018
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40. Stochastic Training of Neural Networks via Successive Convex Approximations.

Author

Scardapane, Simone and Di Lorenzo, Paolo

Subjects
*ARTIFICIAL neural networks, *NONCONVEX programming, *STOCHASTIC convergence
Abstract

This paper proposes a new family of algorithms for training neural networks (NNs). These are based on recent developments in the field of nonconvex optimization, going under the general name of successive convex approximation techniques. The basic idea is to iteratively replace the original (nonconvex, highly dimensional) learning problem with a sequence of (strongly convex) approximations, which are both accurate and simple to optimize. Different from similar ideas (e.g., quasi-Newton algorithms), the approximations can be constructed using only first-order information of the NN function, in a stochastic fashion, while exploiting the overall structure of the learning problem for a faster convergence. We discuss several use cases, based on different choices for the loss function (e.g., squared loss and cross-entropy loss), and for the regularization of the NN’s weights. We experiment on several medium-sized benchmark problems and on a large-scale data set involving simulated physical data. The results show how the algorithm outperforms the state-of-the-art techniques, providing faster convergence to a better minimum. Additionally, we show how the algorithm can be easily parallelized over multiple computational units without hindering its performance. In particular, each computational unit can optimize a tailored surrogate function defined on a randomly assigned subset of the input variables, whose dimension can be selected depending entirely on the available computational power. [ABSTRACT FROM AUTHOR]

Published
2018
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42. Distributed Adaptive Learning of Graph Signals.

Author

Di Lorenzo, Paolo, Banelli, Paolo, Barbarossa, Sergio, and Sardellitti, Stefania

Subjects
*INSTRUCTIONAL systems, *SIGNAL processing, *SAMPLING theorem, *DISTRIBUTED decision making, *FOURIER transforms
Abstract

The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean-square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs. [ABSTRACT FROM PUBLISHER]

Published
2017
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