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Found 8 papers, 0 papers with code
Asymptotically Near-Optimal Hybrid Beamforming for mmWave IRS-Aided MIMO Systems
Thus, it is required to jointly optimize a reflection vector and hybrid beamforming matrices for IRS-aided mmWave MIMO systems.
Near-Field Channel Estimation for XL-RIS Assisted Multi-User XL-MIMO Systems: Hybrid Beamforming Architectures
In this paper, we study the channel estimation problem for XL-RIS assisted multi-user XL-MIMO systems with hybrid beamforming structures.
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm
Robust reinforcement learning (RRL) aims at seeking a robust policy to optimize the worst case performance over an uncertainty set of Markov decision processes (MDPs).
Tighter Regret Analysis and Optimization of Online Federated Learning
In federated learning (FL), it is commonly assumed that all data are placed at clients in the beginning of machine learning (ML) optimization (i. e., offline learning).
Distributed Online Learning with Multiple Kernels
We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion.
Multiple Kernel-Based Online Federated Learning
Online federated learning (OFL) becomes an emerging learning framework, in which edge nodes perform online learning with continuous streaming local data and a server constructs a global model from the aggregated local models.
Distributed Online Learning with Multiple Kernels
Learning a function from such data is of great interest in machine learning tasks for IoT systems.
Pool-based sequential active learning with multi kernels
We study a pool-based sequential active learning (AL), in which one sample is queried at each time from a large pool of unlabeled data according to a selection criterion.
