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Found 28 papers, 0 papers with code
Distributed Intelligent Sensing and Communications for 6G: Architecture and Use Cases
The Distributed Intelligent Sensing and Communication (DISAC) framework redefines Integrated Sensing and Communication (ISAC) for 6G by leveraging distributed architectures to enhance scalability, adaptability, and resource efficiency.
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems.
Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization
This approach, in addition to aligning the semantic representations of different agents, allows compressing the amount of information being exchanged, by appropriately selecting the number of anchors.
Optimizing RIS Impairments through Semantic Communication
This paper investigates how semantic communication can effectively influence and potentially redefine the limitations imposed by physical layer settings.
Semantic Communication Enhanced by Knowledge Graph Representation Learning
This paper investigates the advantages of representing and processing semantic knowledge extracted into graphs within the emerging paradigm of semantic communications.
Soft Partitioning of Latent Space for Semantic Channel Equalization
Semantic channel equalization has emerged as a solution to address language mismatch in multi-user semantic communications.
Latent Space Alignment for Semantic Channel Equalization
We relax the constraint of a shared language between agents in a semantic and goal-oriented communication system to explore the effect of language mismatch in distributed task solving.
Dynamic Relative Representations for Goal-Oriented Semantic Communications
This paper presents a novel framework for goal-oriented semantic communication, leveraging relative representations to mitigate semantic mismatches via latent space alignment.
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.
Towards Distributed and Intelligent Integrated Sensing and Communications for 6G Networks
This paper introduces the distributed and intelligent integrated sensing and communications (DISAC) concept, a transformative approach for 6G wireless networks that extends the emerging concept of integrated sensing and communications (ISAC).
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.
Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting
In this work, we explore the opportunity of DNN splitting at the edge of 6G wireless networks to enable low energy cooperative inference with target delay and accuracy with a goal-oriented perspective.
Reasoning with the Theory of Mind for Pragmatic Semantic Communication
In this paper, a pragmatic semantic communication framework that enables effective goal-oriented information sharing between two-intelligent agents is proposed.
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data.
6G goal-oriented communications: How to coexist with legacy systems?
Specifically, we address the scenario of an eMBB service, i. e., a user uploading a video stream, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task.
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.
Blue Communications for Edge Computing: the Reconfigurable Intelligent Surfaces Opportunity
In line with this vision, this paper proposes an online adaptive method to mitigate the EMFE under end-to-end delay constraints of a computation offloading service, in the context of RIS and multi-access edge computing (MEC)-aided wireless networks.
Energy-Efficient Dynamic Edge Computing with Electromagnetic Field Exposure Constraints
To the best of our knowledge, this is the first work addressing the problem of energy and exposure aware computation offloading.
Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees
Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e. g. sensors), possibly pre-processed (e. g. data compression), and finally processed remotely to output the result of training and/or inference phases.
Effective Goal-oriented 6G Communications: the Energy-aware Edge Inferencing Case
Also, ensemble inference is shown to improve system-wide energy efficiency and even achieve higher goal effectiveness, as compared to the standalone case for some system parameterizations.
Dynamic Edge Computing empowered by Reconfigurable Intelligent Surfaces
In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs).
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.
6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications
The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the correct reception/interpretation of a packet is going to have on the goal accomplishment.
Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing
We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided beyond 5G networks.
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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