| Nome |
# |
|
Distributed signal processing and optimization based on in-network subspace projections, file e3835325-22f2-15e8-e053-a505fe0a3de9
|
274
|
|
Optimal association of mobile users to multi-access edge computing resources, file e383531b-5812-15e8-e053-a505fe0a3de9
|
260
|
|
Joint resource allocation for latency-constrained dynamic computation offloading with MEC, file e3835325-a069-15e8-e053-a505fe0a3de9
|
194
|
|
Latency-constrained dynamic computation offloading with energy harvesting IoT devices, file e3835325-a05e-15e8-e053-a505fe0a3de9
|
178
|
|
Graph-based learning under perturbations via total least-squares, file e3835325-e52f-15e8-e053-a505fe0a3de9
|
174
|
|
Learning and management for internet of things. Accounting for adaptivity and scalability, file e3835320-225f-15e8-e053-a505fe0a3de9
|
120
|
|
Topological signal processing over simplicial complexes, file e3835325-d33b-15e8-e053-a505fe0a3de9
|
115
|
|
Optimal resource allocation in femtocell networks based on Markov modeling of interferers' activity, file e383531d-ce41-15e8-e053-a505fe0a3de9
|
74
|
|
The edge cloud. A holistic view of communication, computation, and caching, file e3835325-d8af-15e8-e053-a505fe0a3de9
|
66
|
|
6G in the sky: On‐demand intelligence at the edge of 3D networks, file e3835328-9c4d-15e8-e053-a505fe0a3de9
|
50
|
|
Beyond private 5G networks: applications, architectures, operator models and technological enablers, file e383532d-f74f-15e8-e053-a505fe0a3de9
|
46
|
|
Dynamic resource optimization for adaptive federated learning empowered by reconfigurable intelligent surfaces, file c3cfb1aa-52a1-478e-aad6-f5633209ab93
|
29
|
|
Topological Slepians: Maximally Localized Representations of Signals Over Simplicial Complexes, file 5901904f-fd9e-4dce-84d0-8b7fabeaa3f4
|
23
|
|
Lyapunov-based optimization of edge resources for energy-efficient adaptive federated learning, file 17bda8a3-d37b-46e8-a80b-e27a74fef086
|
21
|
|
Topological signal processing over weighted simplicial complexes, file c7d10700-795f-4c38-8e07-37bb8050e95e
|
18
|
|
Multiscale causal structure learning, file 51ad7176-6271-47cc-98c5-ba822426e59a
|
17
|
|
Dynamic Ensemble Inference at the Edge, file 438f592a-0511-43b6-8073-7ad473e23c6b
|
16
|
|
Goal-Oriented Communication for Edge Learning Based On the Information Bottleneck, file 51f08e5d-7ea1-4552-8a64-ce5e95e53f64
|
15
|
|
Discontinuous computation offloading for energy-efficient mobile edge computing, file e383532e-6444-15e8-e053-a505fe0a3de9
|
14
|
|
Multi-user goal-oriented communications with energy-efficient edge resource management, file 376423ea-d5c6-4d6d-9b79-f52ee0ac400c
|
12
|
|
Dynamic resource optimization for decentralized estimation in energy harvesting IoT networks, file e3835328-e053-15e8-e053-a505fe0a3de9
|
12
|
|
Distributed signal processing and optimization based on in-network subspace projections, file e3835326-95a1-15e8-e053-a505fe0a3de9
|
7
|
|
Adaptive resource optimization for edge inference with goal-oriented communications, file 89ee454e-5b64-4caf-87b3-b22961dd62d3
|
6
|
|
Sampling and recovery of graph signals, file e383531c-7f9e-15e8-e053-a505fe0a3de9
|
6
|
|
Graph topology inference based on sparsifying transform learning, file e3835320-2261-15e8-e053-a505fe0a3de9
|
6
|
|
Wireless edge machine learning. Resource allocation and trade-offs, file e383532e-75fb-15e8-e053-a505fe0a3de9
|
6
|
|
Dynamic edge computing empowered by reconfigurable intelligent surfaces, file ed8a30b9-c2ab-4bff-99fb-228d8eb3575e
|
5
|
|
Topological Signal Processing Over Generalized Cell Complexes, file 58d8a9e5-419e-47da-8d6f-74251ff5d7a7
|
4
|
|
On the graph Fourier transform for directed graphs, file e383531f-dc53-15e8-e053-a505fe0a3de9
|
4
|
|
Dynamic computation offloading in multi-access edge computing via ultra-reliable and low-latency communications, file e3835325-2158-15e8-e053-a505fe0a3de9
|
4
|
|
Distributed joint optimization of radio and computational resources for mobile cloud computing, file e3835325-d8aa-15e8-e053-a505fe0a3de9
|
4
|
|
Dynamic computation offloading in multi-access edge computing via ultra-reliable and low-latency communications, file e3835327-fe6e-15e8-e053-a505fe0a3de9
|
4
|
|
Graph topology inference based on transform learning, file e3835328-29c3-15e8-e053-a505fe0a3de9
|
4
|
|
Goal-Oriented Communications for the IoT: System Design and Adaptive Resource Optimization, file fb397d84-99d8-4ace-873f-8991f8888536
|
4
|
|
Joint optimization of radio and computational resources for multicell mobile-edge computing, file e383531d-f0a6-15e8-e053-a505fe0a3de9
|
3
|
|
Distributed adaptive learning of graph signals, file e383531e-56bb-15e8-e053-a505fe0a3de9
|
3
|
|
6G: The next frontier. From holographic messaging to artificial intelligence using subterahertz and visible light communication, file e3835322-d65d-15e8-e053-a505fe0a3de9
|
3
|
|
Fast distributed average consensus algorithms based on advection-diffusion processes, file e3835324-1518-15e8-e053-a505fe0a3de9
|
3
|
|
Enabling prediction via multi-layer graph inference and sampling, file e3835325-aa1b-15e8-e053-a505fe0a3de9
|
3
|
|
Dynamic resource allocation for wireless edge machine learning with latency and accuracy guarantees, file e3835325-c3ae-15e8-e053-a505fe0a3de9
|
3
|
|
Distributed signal recovery based on in-network subspace projections, file e3835325-d33a-15e8-e053-a505fe0a3de9
|
3
|
|
Semantic Communications Based on Adaptive Generative Models and Information Bottleneck, file b26e3fda-f7d1-4ff4-b267-5b21bb2a0c4f
|
2
|
|
Reducing the in band network telemetry overhead through the spatial sampling. Theory and experimental results, file b6efae75-eecf-4f5b-af03-69d12c097553
|
2
|
|
Communicating while computing: Distributed mobile cloud computing over 5G heterogeneous networks, file e3835312-6b1d-15e8-e053-a505fe0a3de9
|
2
|
|
Swarming algorithms for distributed radio resource allocation: a further step in the direction of an ever-deeper synergism between biological mathematical modeling and signal processing, file e383531a-f0a0-15e8-e053-a505fe0a3de9
|
2
|
|
Distributed detection and estimation in wireless sensor networks, file e383531c-75a4-15e8-e053-a505fe0a3de9
|
2
|
|
Optimal beamforming for range-doppler ambiguity minimization in squinted SAR, file e383531c-92ec-15e8-e053-a505fe0a3de9
|
2
|
|
Parameter estimation of 2D multi-component polynomial phase signals: an application to SAR imaging of moving targets, file e383531c-9db6-15e8-e053-a505fe0a3de9
|
2
|
|
Adaptation and learning over complex networks, file e383531d-dc96-15e8-e053-a505fe0a3de9
|
2
|
|
Graph signal processing in the presence of topology uncertainties, file e3835324-d84f-15e8-e053-a505fe0a3de9
|
2
|
|
Joint optimization of radio and computational resources for multicell mobile cloud computing, file e3835325-a6d8-15e8-e053-a505fe0a3de9
|
2
|
|
Joint cell selection and radio resource allocation in MIMO small cell networks via successive convex approximation, file e3835325-c64a-15e8-e053-a505fe0a3de9
|
2
|
|
Joint optimization of collaborative sensing and radio resource allocation in small-cell networks, file e3835327-98bd-15e8-e053-a505fe0a3de9
|
2
|
|
Optimal topology control and power allocation for minimum energy consumption in consensus networks, file e3835327-c809-15e8-e053-a505fe0a3de9
|
2
|
|
Dynamic resource optimization for decentralized signal estimation in energy harvesting wireless sensor networks, file e3835328-122f-15e8-e053-a505fe0a3de9
|
2
|
|
2014 IEEE Signal Processing Society Best Paper Award, file e3835329-caa5-15e8-e053-a505fe0a3de9
|
2
|
|
2020 IEEE Signal Processing Society Best Paper Award, file e3835329-d301-15e8-e053-a505fe0a3de9
|
2
|
|
Online learning of time-varying signals and graphs, file e383532d-feb7-15e8-e053-a505fe0a3de9
|
2
|
|
Dynamic Resource Allocation for Multi-User Goal-oriented Communications at the Wireless Edge, file 19e0b54a-435c-4308-8b95-f6bd1c807b12
|
1
|
|
Analog versus digital pulse amplitude modulation for goal-oriented wireless communications, file 663ab502-ff96-43a9-8dcd-43f8b0bdca88
|
1
|
|
In Band Network Telemetry Overhead Reduction Based on Data Flows Sampling and Recovering, file 8e496209-9a4e-46ab-ba6b-0e5f678c073d
|
1
|
|
5G-MiEdge: Design, standardization and deployment of 5G phase II technologies: MEC and mmWaves joint development for Tokyo 2020 olympic games, file e3835319-6542-15e8-e053-a505fe0a3de9
|
1
|
|
Graph Fourier transform for directed graphs based on Lovász extension of min-cut, file e3835319-6818-15e8-e053-a505fe0a3de9
|
1
|
|
Enabling effective mobile edge computing using millimeterwave links, file e3835319-681a-15e8-e053-a505fe0a3de9
|
1
|
|
Optimal sampling strategies for adaptive learning of graph signals, file e3835319-681b-15e8-e053-a505fe0a3de9
|
1
|
|
Distributed recursive least squares strategies for adaptive reconstruction of graph signals, file e3835319-681c-15e8-e053-a505fe0a3de9
|
1
|
|
Overbooking radio and computation resources in mmW-mobile edge computing to reduce vulnerability to channel intermittency, file e3835319-6f3c-15e8-e053-a505fe0a3de9
|
1
|
|
Distributed sum-rate maximization over finite rate coordination links affected by random failures, file e383531b-0b20-15e8-e053-a505fe0a3de9
|
1
|
|
Adaptive Least Mean Squares Estimation of Graph Signals, file e383531b-4dbe-15e8-e053-a505fe0a3de9
|
1
|
|
Small cell clustering for efficient distributed fog computing: A multi-user case, file e383531c-5a6d-15e8-e053-a505fe0a3de9
|
1
|
|
The fog balancing: Load distribution for small cell cloud computing, file e383531c-5dd2-15e8-e053-a505fe0a3de9
|
1
|
|
Decentralized resource assignment in cognitive networks based on swarming mechanisms over random graphs, file e383531c-7fae-15e8-e053-a505fe0a3de9
|
1
|
|
Bio-inspired decentralized radio access based on swarming mechanisms over adaptive networks, file e383531c-8390-15e8-e053-a505fe0a3de9
|
1
|
|
Adaptive graph signal processing: algorithms and optimal sampling strategies, file e383531c-92dd-15e8-e053-a505fe0a3de9
|
1
|
|
On the degrees of freedom of signals on graphs, file e383531c-a577-15e8-e053-a505fe0a3de9
|
1
|
|
A bio-inspired swarming algorithm for decentralized access in cognitive radio, file e383531c-b4d7-15e8-e053-a505fe0a3de9
|
1
|
|
Introduction to the issue on adaptation and learning over complex networks, file e383531d-d2f7-15e8-e053-a505fe0a3de9
|
1
|
|
Where, when, and how mmWave is used in 5G and beyond, file e383531d-faa0-15e8-e053-a505fe0a3de9
|
1
|
|
Small perturbation analysis of network topologies, file e3835321-e4a2-15e8-e053-a505fe0a3de9
|
1
|
|
Robust graph signal processing in the presence of uncertainties on graph topology, file e3835322-1643-15e8-e053-a505fe0a3de9
|
1
|
|
Signal and graph perturbations via total least-squares, file e3835322-1645-15e8-e053-a505fe0a3de9
|
1
|
|
Dynamic joint resource allocation and user assignment in multi-access edge computing, file e3835322-6aa6-15e8-e053-a505fe0a3de9
|
1
|
|
Learning from signals defined over simplicity complexes, file e3835322-8abe-15e8-e053-a505fe0a3de9
|
1
|
|
Computation offloading strategies based on energy minimization under computational rate constraints, file e3835322-ac08-15e8-e053-a505fe0a3de9
|
1
|
|
Fast simulation performance evaluation of spaceborne SAR-GMTI missions for maritime applications, file e3835323-7b9f-15e8-e053-a505fe0a3de9
|
1
|
|
Decentralized estimation and control of algebraic connectivity of random ad-hoc networks, file e3835323-7bac-15e8-e053-a505fe0a3de9
|
1
|
|
On sparse controllability of graph signals, file e3835324-d3f3-15e8-e053-a505fe0a3de9
|
1
|
|
Distributed adaptive learning of signals defined over graphs, file e3835325-110a-15e8-e053-a505fe0a3de9
|
1
|
|
Network energy efficient mobile edge computing with reliability guarantees, file e3835325-85e3-15e8-e053-a505fe0a3de9
|
1
|
|
Distributed adaptive learning of graph processes via in-network subspace projections, file e3835325-d2ed-15e8-e053-a505fe0a3de9
|
1
|
|
6G in the sky: On‐demand intelligence at the edge of 3D networks, file e3835328-545d-15e8-e053-a505fe0a3de9
|
1
|
|
Topological signal processing: Making sense of data building on multiway relations, file e3835329-019d-15e8-e053-a505fe0a3de9
|
1
|
|
6G networks. Beyond Shannon towards semantic and goal-oriented communications, file e383532b-5834-15e8-e053-a505fe0a3de9
|
1
|
| Totale |
1.882 |