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Found 7 papers, 0 papers with code
Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals
Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks.
Distributed Learning for Time-varying Networks: A Scalable Design
The wireless network is undergoing a trend from "onnection of things" to "connection of intelligence".
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks
We not only verify the performance gain achieved, but also provide implementation-friend designs, i. e., a scalable neural network design for the agent and a virtual environment training framework.
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate.
Realistic Channel Models Pre-training
In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models.
Deep Reinforcement Learning for Scheduling in Cellular Networks
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia.
Reinforcement Learning for Nested Polar Code Construction
In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques.
