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Distributed Intelligent Integrated Sensing and Communications: The 6G-DISAC Approach
This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures.
Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents.
Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors
The main contribution of this paper is two-fold.
Semantic Channel Equalizer: Modelling Language Mismatch in Multi-User Semantic Communications
To address this problem, this paper proposes a new semantic channel equalizer to counteract and limit the critical ambiguity in message interpretation.
Sensing of Side Lobes Interference for Blockage Prediction in Dense mmWave Networks
The integration of sensing capability in the design of wireless communication systems is foreseen as a key enabler for efficient radio resource management in next-generation networks.
Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks
This paper addresses the efficient management of Mobile Access Points (MAPs), which are Unmanned Aerial Vehicles (UAV), in 5G networks.
Learning Semantics: An Opportunity for Effective 6G Communications
Back to Shannon's information theory, the goal of communication has long been to guarantee the correct reception of transmitted messages irrespective of their meaning.
Transferable and Distributed User Association Policies for 5G and Beyond Networks
We study the problem of user association, namely finding the optimal assignment of user equipment to base stations to achieve a targeted network performance.
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning
Then, based on Lyapunov stochastic optimization tools, we decouple the formulated problem into a CPU scheduling problem and a radio resource allocation problem to be solved in a per-slot basis.
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks
Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks.
Multi-agent Reinforcement Learning
reinforcement-learning
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