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Found 15 papers, 2 papers with code
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model
Specifically, a latency minimization problem is first proposed to jointly optimize the data offloading ratio, transmit power allocation, and computing resource allocation.
When UAV Meets Federated Learning: Latency Minimization via Joint Trajectory Design and Resource Allocation
Federated learning (FL) has emerged as a pivotal solution for training machine learning models over wireless networks, particularly for Internet of Things (IoT) devices with limited computation resources.
Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access
Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency.
Energy-Efficient Multi-UAV-Enabled MEC Systems with Space-Air-Ground Integrated Networks
With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computation-intensive and latency-sensitive tasks.
UAV-Enabled Data Collection for IoT Networks via Rainbow Learning
This letter considers a scenario where a multi-antenna UAV is dispatched to simultaneously collect data from multiple ground IoT nodes (GNs) within a time interval.
Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning
Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation.
Movable-Antenna Array Empowered ISAC Systems for Low-Altitude Economy
This paper investigates a movable-antenna (MA) array empowered integrated sensing and communications (ISAC) over low-altitude platform (LAP) system to support low-altitude economy (LAE) applications.
Joint Association, Beamforming, and Resource Allocation for Multi-IRS Enabled MU-MISO Systems With RSMA
Intelligent reflecting surface (IRS) and rate-splitting multiple access (RSMA) technologies are at the forefront of enhancing spectrum and energy efficiency in the next generation multi-antenna communication systems.
UAV-Enabled Wireless Networks with Movable-Antenna Array: Flexible Beamforming and Trajectory Design
Recently, movable antenna (MA) array becomes a promising technology for improving the communication quality in wireless communication systems.
Joint Signal Detection and Automatic Modulation Classification via Deep Learning
Different from prior works that investigate them independently, this paper studies the joint signal detection and automatic modulation classification (AMC) by considering a realistic and complex scenario, in which multiple signals with different modulation schemes coexist at different carrier frequencies.
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality
In contrast, our study first illustrates the benefits of incorporating a natural geometric structure within a linear regression model, which corresponds to the generalized eigenvalue problem formed by the Gram matrices of both domains.
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff
Imputation methods in time-series data often are applied to the full panel data with the purpose of training a model for a downstream out-of-sample task.
Distributionally Robust Parametric Maximum Likelihood Estimation
We consider the parameter estimation problem of a probabilistic generative model prescribed using a natural exponential family of distributions.
Machine Learning's Dropout Training is Distributionally Robust Optimal
This paper shows that dropout training in Generalized Linear Models is the minimax solution of a two-player, zero-sum game where an adversarial nature corrupts a statistician's covariates using a multiplicative nonparametric errors-in-variables model.
Latent Variable Discovery Using Dependency Patterns
The causal discovery of Bayesian networks is an active and important research area, and it is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data.
