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Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach
Ultra-reliable low-latency communication (URLLC) is the cornerstone for a broad range of emerging services in next-generation wireless networks.
Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation
For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation.
Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN
To solve this problem, a new solution is proposed based on evolutionary-based deep reinforcement learning (EDRL) to accelerate and optimize the slice management learning process in the radio access network's (RAN) intelligent controller (RIC) modules.
Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks
In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency.
Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)
In this paper, to characterize the wireless connectivity performance for UAM, a spatial model is proposed.
Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks
To address these challenges, in this paper, a novel semantic-aware CDRL method is proposed to enable a group of heterogeneous untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.
Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communications
The results also show that the proposed scheme can yield up to 75% performance gain, in terms of spectral efficiency, compared to the conventional hierarchical beam training with a fixed number of training levels.
Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles
The results also validate the feasibility of the contract-theoretic incentive mechanism and show that the proposed mechanism can improve the convergence speed of the DFP algorithm by 40% compared to the baselines.
Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks
Emerging wireless services with extremely high data rate requirements, such as real-time extended reality applications, mandate novel solutions to further increase the capacity of future wireless networks.
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition.
Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks
The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' locations and orientations.
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