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Found 13 papers, 4 papers with code
Deep Convolutional Compression for Massive MIMO CSI Feedback
In comparison with previous works, the main contributions of DeepCMC are two-fold: i) DeepCMC is fully convolutional, and it can be used in a wide range of scenarios with various numbers of sub-channels and transmit antennas; ii) DeepCMC includes quantization and entropy coding blocks and minimizes a cost function that accounts for both the rate of compression and the reconstruction quality of the channel matrix at the BS.
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems
Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains.
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback
We also propose a distributed version of DeepCMC for a multi-user MIMO scenario to encode and reconstruct the CSI from multiple users in a distributed manner.
Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems
Our pruning-based pilot reduction technique reduces the overhead by allocating pilots across subcarriers non-uniformly and exploiting the inter-frequency and inter-antenna correlations in the channel matrix efficiently through convolutional layers and attention module.
Supervised Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise without Simultaneous Acceleration Signals
A major contribution of this work is that it alleviates the need to use simultaneous acceleration signals.
FedRec: Federated Learning of Universal Receivers over Fading Channels
Wireless communications is often subject to channel fading.
Federated mmWave Beam Selection Utilizing LIDAR Data
Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection.
LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility.
Deep Extended Feedback Codes
A new deep-neural-network (DNN) based error correction encoder architecture for channels with feedback, called Deep Extended Feedback (DEF), is presented in this paper.
DRF Codes: Deep SNR-Robust Feedback Codes
We present a new deep-neural-network (DNN) based error correction code for fading channels with output feedback, called deep SNR-robust feedback (DRF) code.
Age of Information in Federated Learning over Wireless Networks
In this paper, federated learning (FL) over wireless networks is investigated.
Collaborative Learning with a Drone Orchestrator
For this system, the convergence rate of collaborative learning is derived while considering data heterogeneity, sensor noise levels, and communication errors, then, the drone trajectory that maximizes the final accuracy of the trained NN is obtained.
MU-Massive MIMO with Multiple RISs: SINR Maximization and Asymptotic Analysis
In this letter, we investigate the signal-to-interference-plus-noise-ratio (SINR) maximization problem in a multi-user massive multiple-input-multiple-output (massive MIMO) system enabled with multiple reconfigurable intelligent surfaces (RISs).
