Distributed Diffusion-based LMS for Node-Specific Parameter Estimation over Adaptive Networks

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local or global parameters. Although all the different LMS algorithms are coupled, the diffusion-based implementation of each LMS algorithm is exclusively undertaken by the nodes of the network interested in a specific set of local or global parameters. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.Comment: 5 pages, 2 figures, Published in Proc. IEEE ICASSP, Florence, Italy, May 201

Optimal power control in Cognitive MIMO systems with limited feedback

In this paper, the problem of optimal power allocation in Cognitive Radio (CR) Multiple Input Multiple Output (MIMO) systems is treated. The focus is on providing limited feedback solutions aiming at maximizing the secondary system rate subject to a constraint on the average interference caused to primary communication. The limited feedback solutions are obtained by reducing the information available at secondary transmitter (STx) for the link between STx and the secondary receiver (SRx) as well as by limiting the level of available information at STx that corresponds to the link between the STx and the primary receiver PRx. Monte Carlo simulation results are given that allow to quanitfy the performance achieved by the proposed algorithms

Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest, parameters of common interest to a subset of nodes and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. Coupled with the estimation of the different sets of parameters, the implementation of each LMS algorithm is only undertaken by the nodes of the network interested in a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.Comment: 13 pages, 6 figure

Deep Equilibrium Models Meet Federated Learning

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due to the sharing large models and the ability to incorporate heterogeneous edge devices with significantly different computation capabilities. Additionally, a weighted average fusion rule is proposed at the server-side of the FL framework to account for the different qualities of models from heterogeneous edge devices. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning, contributing to the development of an efficient and effective FL framework. Finally, promising initial experimental results are presented, demonstrating the potential of this approach in addressing challenges of FL.Comment: The paper has been accepted for publication in European Signal Processing Conference, Eusipco 202

EFFICIENT DECISION FEEDBACK EQUALIZER FOR SPARSE MULTIPATH CHANNELS

In this paper a computationally e cient Decision Feedback Equalizer (DFE) is proposed. The new equalizer is appropriate for channels with long and sparse impulse response (IR) as those encountered in many wireless communications applications. The main feature of the algorithm is that the actual size of the computationally demanding feedback lter is signi cantly reduced. This is achieved by exploiting the particular form of the multipath channel to derive a tractable expression for the causal part of the overall discrete channel IR (including the feedforward lter). Based on the above expression the feedback lter can be built so as to act only to a properly selected set of tap positions. The new DFE exhibits considerable computational savings and faster convergence as compared to the conventional DFE, o ering the same steady-state performance. 1

Une version fréquentielle de l'égaliseur à retour de décision pour la correction des canaux à réponse impulsionnelle longue

Cet article propose une version bloc et fréquentielle de l'égaliseur à retour de décision (B-ERD). La partie filtrage de cet égaliseur est optimisée afin d'être réalisable dans un circuit intégré alors que la partie mise à jour des coefficients optimise le nombre d'opérations. La formulation en bloc dans le domaine fréquentiel utilise des tailles de bloc différentes pour chaque partie. Cet égaliseur associe la faible charge de calcul des techniques de filtrage rapide aux très bonnes performances de l'égaliseur à retour de décision ce qui le rend particulièrement bien adapté à la correction des canaux à réponse impulsionnelle longue