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Found 16 papers, 1 papers with code
WDMoE: Wireless Distributed Large Language Models with Mixture of Experts
In this paper, we propose a wireless distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE, deploying LLMs collaboratively across edge servers of base station (BS) and mobile devices in the wireless communications system.
Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems
This letter considers the transceiver design in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high-quality data transmission.
Optimization of RIS Placement for Satellite-to-Ground Coverage Enhancement
In satellite-to-ground communication, ensuring reliable and efficient connectivity poses significant challenges.
How to Differentiate between Near Field and Far Field: Revisiting the Rayleigh Distance
Future wireless communication systems are likely to adopt extremely large aperture arrays and millimeter-wave/sub-THz frequency bands to achieve higher throughput, lower latency, and higher energy efficiency.
Hierarchical Beam Alignment for Millimeter-Wave Communication Systems: A Deep Learning Approach
Fast and precise beam alignment is crucial for high-quality data transmission in millimeter-wave (mmWave) communication systems, where large-scale antenna arrays are utilized to overcome the severe propagation loss.
Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization
To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity
This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links.
Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems
In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner.
Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning
In contrast, split learning (SL) can reduce the computing load of devices by using model splitting and assignment, but increase the communication burden to transmit intermediate results.
Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems
The intrinsic mechanism of neural network based digital modulation is mapping continuous output of the neural network encoder into discrete constellation symbols, which is a non-differentiable function that cannot be trained with existing gradient descend algorithms.
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach
One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters.
Double-Sided Information Aided Temporal-Correlated Massive Access
This letter considers temporal-correlated massive access, where each device, once activated, is likely to transmit continuously over several consecutive frames.
Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data
In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter.
Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning
In FL, however, the model update is an indirect multi-terminal source coding problem, also called as the CEO problem where each edge device cannot observe directly the gradient that is to be reconstructed at the decoder, but is rather provided only with a noisy version.
Deep Learning for Wireless Coded Caching with Unknown and Time-Variant Content Popularity
This approach, on one hand, can learn the caching policy in continuous action space by using the actor-critic architecture.
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning
We obtain the optimal policy in closed form when gradient statistics are given.
