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Found 15 papers, 0 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.
Inter-RIS Beam Focusing Codebook Design in Cooperative Distributed RIS Systems
This paper explores distributed Reconfigurable Intelligent Surfaces (RISs) by introducing a cooperative dimension that enhances adaptability and performance.
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function
In this work, we introduce a zero-order distributed optimization method based on a one-point estimate of the gradient tracking technique.
Communication and Energy Efficient Federated Learning using Zero-Order Optimization Technique
Federated learning (FL) is a popular machine learning technique that enables multiple users to collaboratively train a model while maintaining the user data privacy.
Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method
We then offer a thorough analysis of the proposed zero-order federated learning (ZOFL) framework and prove that our method converges \textit{almost surely}, which is a novel result in nonconvex ZO optimization.
Zero-Order One-Point Estimate with Distributed Stochastic Gradient-Tracking Technique
We analyze the convergence of this novel technique for smooth and convex objectives using stochastic approximation tools, and we prove that it converges almost surely to the optimum.
Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet
Channel state information (CSI) is of pivotal importance as it enables wireless systems to adapt transmission parameters more accurately, thus improving the system's overall performance.
Artificial Intelligence for 6G Networks: Technology Advancement and Standardization
With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks.
A Novel Algorithm to Report CSI in MIMO-Based Wireless Networks
Besides, in the immobile radio channel, feedback can be eliminated, which brings the benefit of further reducing the OTA overhead.
Dealing with CSI Compression to Reduce Losses and Overhead: An Artificial Intelligence Approach
The proposed scheme enhances the CSI compression, which is done at the mobile terminal (MT), along with accurate recovery of estimated CSI at the BS.
Semantic Communications in Networked Systems: A Data Significance Perspective
We present our vision for a departure from the established way of architecting and assessing communication networks, by incorporating the semantics of information for communications and control in networked systems.
On the Global Optimality of Whittle's index policy for minimizing the age of information
This paper examines the average age minimization problem where only a fraction of the network users can transmit simultaneously over unreliable channels.
Information Theory Networking and Internet Architecture Information Theory
The Age of Incorrect Information: an Enabler of Semantics-Empowered Communication
Interestingly, we show that the AoII-optimal policy is also error-optimal for the adopted information source model.
Information Theory Information Theory
Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach
In the proposed framework, we model the online power control problem as a discrete-time mean-field game (MFG), and analytically show that the MFG has a unique stationary solution.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Deep Learning Based Online Power Control for Large Energy Harvesting Networks
In this paper, we propose a deep learning based approach to design online power control policies for large EH networks, which are often intractable stochastic control problems.
