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Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G
The resulting large-scale complicated network optimization problems are beyond the capability of model-based theoretical methods due to the overwhelming computational complexity and the long processing time.
Asynchronous Wireless Federated Learning with Probabilistic Client Selection
Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server.
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden.
Collaborative Driving: Learning- Aided Joint Topology Formulation and Beamforming
Currently, autonomous vehicles are able to drive more naturally based on the driving policies learned from millions of driving miles in real environments.
See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS
Based on the Gaussian distribution of traffic flow, a hybrid model with a Bayesian learning algorithm is developed which can effectively expand the application scenarios of SARIMA.
