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Found 12 papers, 1 papers with code
Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems
In this method, the magnitude of the latent vector is quantized using a non-uniform scalar codebook with a proper transformation function, while the direction of the latent vector is quantized using a trainable Grassmannian codebook.
Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication
The third framework is designed to accommodate the non-coherent scheme involving a small number of data bits, which simultaneously performs AD and DD.
Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications
In semantic communication systems with digital modulation and demodulation, robust design of JSCC encoder and decoder becomes challenging not only due to the unpredictable dynamics of channel conditions but also due to diverse modulation orders.
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks
Moreover, an error feedback strategy is introduced to compensate for both compression and reconstruction errors.
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression
In the second strategy, the non-dropped intermediate feature and gradient vectors are quantized using adaptive quantization levels determined based on the ranges of the vectors.
MIMO Detection under Hardware Impairments: Learning with Noisy Labels
By using the outputs of coarse data detection as noisy training data, the model-driven method avoids the need for additional training overhead beyond traditional pilot overhead for channel estimation.
MetaSSD: Meta-Learned Self-Supervised Detection
There are two major limitations in the supervised approaches: a) a model needs to be retrained from scratch when new train symbols come to adapt to a new channel status, and b) the length of the training symbols needs to be longer than a certain threshold to make the model generalize well on unseen symbols.
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning
Data-aided channel estimation is a promising solution to improve channel estimation accuracy by exploiting data symbols as pilot signals for updating an initial channel estimate.
Communication-Efficient Federated Learning via Quantized Compressed Sensing
We also present a low-complexity approach for the gradient reconstruction.
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP).
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems
One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices.
Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach
The key idea is to exploit input-output samples obtained from data detection, to compensate the mismatch in the likelihood function.
