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Found 42 papers, 4 papers with code
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.
MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification
We present MERLOT, a scalable mixture-of-expert (MoE) based refinement of distilled large language model optimized for encrypted traffic classification.
Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach
Optimizing the deployment of large language models (LLMs) in edge computing environments is critical for enhancing privacy and computational efficiency.
Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G
Existing distributed learning frameworks like Federated Learning and Split Learning often struggle with significant challenges in dynamic network environments including high synchronization demands, costly communication overhead, severe computing resource consumption, and data heterogeneity across network nodes.
Topology Data Analysis-based Error Detection for Semantic Image Transmission with Incremental Knowledge-based HARQ
In particular, different from the conventional CRC, we introduce a topological data analysis (TDA)-based error detection method, which capably digs out the inner topological and geometric information of images, so as to capture semantic information and determine the necessity for re-transmission.
Interplay of Semantic Communication and Knowledge Learning
In the swiftly advancing realm of communication technologies, Semantic Communication (SemCom), which emphasizes knowledge understanding and processing, has emerged as a hot topic.
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles
In this study, we try to address the decision-making problem of multiple CAVs with limited communications and propose a decentralized Multi-Agent Probabilistic Ensembles with Trajectory Sampling algorithm MA-PETS.
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning
In this paper, we propose a novel decomposition-based multi-agent distributional RL method by approximating the globally shared noisy reward by a Gaussian mixture model (GMM) and decomposing it into the combination of individual distributional local rewards, with which each agent can be updated locally through distributional RL.
Distributional Reinforcement Learning
Multi-agent Reinforcement Learning
+3
Self-Critical Alternate Learning based Semantic Broadcast Communication
In particular, to enable stable optimization via a nondifferentiable semantic metric, we regard sentence similarity as a reward and formulate this learning process as an RL problem.
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance
Multi-Robot System (MRS) has garnered widespread research interest and fostered tremendous interesting applications, especially in cooperative control fields.
Alternate Learning based Sparse Semantic Communications for Visual Transmission
Semantic communication (SemCom) demonstrates strong superiority over conventional bit-level accurate transmission, by only attempting to recover the essential semantic information of data.
Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception
Collaborative perception by leveraging the shared semantic information plays a crucial role in overcoming the individual limitations of isolated agents.
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication
Adaptive multi-agent formation control, which requires the formation to flexibly adjust along with the quantity variations of agents in a decentralized manner, belongs to one of the most challenging issues in multi-agent systems, especially under communication-limited constraints.
NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services
Towards personalized generative services, a collaborative cloud-edge methodology is promising, as it facilitates the effective orchestration of heterogeneous distributed communication and computing resources.
RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning
The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency.
Knowledge Enhanced Semantic Communication Receiver
In recent years, with the rapid development of deep learning and natural language processing technologies, semantic communication has become a topic of great interest in the field of communication.
Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction
On top of that, we customize its temporal and structural learning modules to further boost the prediction performance.
Semantics-Empowered Communication: A Tutorial-cum-Survey
Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e. g., theories, applications, metrics and implementations) in both academia and industry.
AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching
In this paper, to maximize the cache hit rate, we leverage an effective dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph.
Contrastive Monotonic Pixel-Level Modulation
Continuous one-to-many mapping is a less investigated yet important task in both low-level visions and neural image translation.
Adaptive Bit Rate Control in Semantic Communication with Incremental Knowledge-based HARQ
Semantic communication has witnessed a great progress with the development of natural language processing (NLP) and deep learning (DL).
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning
The paper considers independent reinforcement learning (IRL) for multi-agent decision-making process in the paradigm of federated learning (FL).
Rethinking Modern Communication from Semantic Coding to Semantic Communication
Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message.
Reinforcement Learning-powered Semantic Communication via Semantic Similarity
We introduce a new semantic communication mechanism - SemanticRL, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision.
Semantic Communication with Adaptive Universal Transformer
With the development of deep learning (DL), natural language processing (NLP) makes it possible for us to analyze and understand a large amount of language texts.
Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty
Online reinforcement learning (RL) has been widely applied in information processing scenarios, which usually exhibit much uncertainty due to the intrinsic randomness of channels and service demands.
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication
The paper considers independent reinforcement learning (IRL) for multi-agent collaborative decision-making in the paradigm of federated learning (FL).
Learning to Prune in Training via Dynamic Channel Propagation
In this paper, we propose a novel network training mechanism called "dynamic channel propagation" to prune the neural networks during the training period.
KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis
Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications.
Stigmergic Independent Reinforcement Learning for Multi-Agent Collaboration
With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents, so as to gradually approach the final collective objective through continuously learning from the environment based on their individual observations.
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing
Furthermore, as DPGD only works in continuous action space, we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output.
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing
Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function.
Deep Reinforcement Learning
Distributional Reinforcement Learning
+4
Internet of Intelligence: The Collective Advantage for Advancing Communications and Intelligence
Afterwards, we highlight the potential huge impact of CI on both communications and intelligence.
Evaluation Mechanism of Collective Intelligence for Heterogeneous Agents Group
Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action.
Brain-Inspired Stigmergy Learning
Stigmergy has proved its great superiority in terms of distributed control, robustness and adaptability, thus being regarded as an ideal solution for large-scale swarm control problems.
Deep Learning with Long Short-Term Memory for Time Series Prediction
Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values.
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks
Software Defined Internet of Things (SD-IoT) Networks profits from centralized management and interactive resource sharing which enhances the efficiency and scalability of IoT applications.
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices.
Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory
So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost.
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network
As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management.
The Learning and Prediction of Application-level Traffic Data in Cellular Networks
Afterwards, with the aid of the traffic "big data", we make a comprehensive study over the modeling and prediction framework of cellular network traffic.
TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks
Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs).
