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Found 16 papers, 0 papers with code
Task-Oriented Edge Networks: Decentralized Learning Over Wireless Fronthaul
Inspired by the nomographic function, an efficient cloud inference model becomes an integration of a number of shallow DNNs.
Joint Precoding and Fronthaul Compression for Cell-Free MIMO Downlink With Radio Stripes
Due to the serial transfer on radio stripes, each AP has an access to all the compressed blocks which pass through it.
Learning Decentralized Power Control in Cell-Free Massive MIMO Networks
This paper studies learning-based decentralized power control methods for cell-free massive multiple-input multiple-output (MIMO) systems where a central processor (CP) controls access points (APs) through fronthaul coordination.
Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding
To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS).
A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization
However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i. e., the number of antennas or users.
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission
This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a cloud server (CS) through distributed access points (APs).
Deep Learning Based Resource Assignment for Wireless Networks
This paper studies a deep learning approach for binary assignment problems in wireless networks, which identifies binary variables for permutation matrices.
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks
An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies.
Learning Autonomy in Management of Wireless Random Networks
The DMPNN is investigated for various configurations of the power control in wireless networks, and intensive numerical results prove its universality and viability over conventional optimization and DNN approaches.
Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks
Fog radio access networks (F-RANs), which consist of a cloud and multiple edge nodes (ENs) connected via fronthaul links, have been regarded as promising network architectures.
Learning Robust Beamforming for MISO Downlink Systems
This paper investigates a learning solution for robust beamforming optimization in downlink multi-user systems.
Deep Learning Methods for Universal MISO Beamforming
This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station.
Deep Learning-based Limited Feedback Designs for MIMO Systems
We study a deep learning (DL) based limited feedback methods for multi-antenna systems.
A Deep Learning Approach to Universal Binary Visible Light Communication Transceiver
For universal support of arbitrary dimming target, the DL-based VLC transceiver is trained with multiple dimming constraints, which turns out to be a constrained training optimization that is very challenging to handle with existing DL methods.
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management
This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations.
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate.
