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Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission.
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation
Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability.
Gossiped and Quantized Online Multi-Kernel Learning
In instances of online kernel learning where little prior information is available and centralized learning is unfeasible, past research has shown that distributed and online multi-kernel learning provides sub-linear regret as long as every pair of nodes in the network can communicate (i. e., the communications network is a complete graph).
Distributed Q-Learning with State Tracking for Multi-agent Networked Control
Specifically, we assume that agents maintain local estimates of the global state based on their local information and communications with neighbors.
Two-Hop Multi-UAV Relay Network Optimization with Directional Antennas
In this paper, we consider the multi-UAV deployment problem for a two-hop relaying system.
Using Quantization to Deploy Heterogeneous Nodes in Two-Tier Wireless Sensor Networks
We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs).
Information Theory Information Theory
