A tutorial on nyusim: Sub-terahertz and millimeter-wave channel simulator for 5G, 6G and beyond
With the advancement of wireless communication to sub-terahertz (THz) and millimeter-
wave (mmWave) bands, accurate channel models and simulation tools are becoming …
wave (mmWave) bands, accurate channel models and simulation tools are becoming …
Wireless deep video semantic transmission
In this paper, we design a new class of high-efficiency deep joint source-channel coding
methods to achieve end-to-end video transmission over wireless channels. The proposed …
methods to achieve end-to-end video transmission over wireless channels. The proposed …
Domain generalization in machine learning models for wireless communications: Concepts, state-of-the-art, and open issues
Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …
next-generation wireless systems. This led to a large body of research work that applies ML …
Sionna RT: Differentiable ray tracing for radio propagation modeling
Sionna™ is a GPU-accelerated open-source library for link-level simulations based on
TensorFlow. Since release v0. 14 it integrates a differentiable ray tracer (RT) for the …
TensorFlow. Since release v0. 14 it integrates a differentiable ray tracer (RT) for the …
WiThRay: A versatile ray-tracing simulator for smart wireless environments
H Choi, J Oh, J Chung, GC Alexandropoulos… - IEEE Access, 2023 - ieeexplore.ieee.org
Channel simulators capitalizing on the ray-tracing (RT) principle are widely used in wireless
communications for generating realistic channel data. By combining the RT algorithm for the …
communications for generating realistic channel data. By combining the RT algorithm for the …
Graph neural networks for channel decoding
In this work, we propose a fully differentiable graph neural network (GNN)-based
architecture for channel decoding and showcase a competitive decoding performance for …
architecture for channel decoding and showcase a competitive decoding performance for …
Improved nonlinear transform source-channel coding to catalyze semantic communications
Recent deep learning methods have led to increased interest in solving high-efficiency end-
to-end transmission problems. These methods, we call nonlinear transform source-channel …
to-end transmission problems. These methods, we call nonlinear transform source-channel …
Semantic information recovery in wireless networks
Motivated by the recent success of Machine Learning (ML) tools in wireless
communications, the idea of semantic communication by Weaver from 1949 has gained …
communications, the idea of semantic communication by Weaver from 1949 has gained …
Forking uncertainties: Reliable prediction and model predictive control with sequence models via conformal risk control
In many real-world problems, predictions are leveraged to monitor and control cyber-
physical systems, demanding guarantees on the satisfaction of reliability and safety …
physical systems, demanding guarantees on the satisfaction of reliability and safety …
Toward adaptive semantic communications: Efficient data transmission via online learned nonlinear transform source-channel coding
The emerging field semantic communication is driving the research of end-to-end data
transmission. By utilizing the powerful representation ability of deep learning models …
transmission. By utilizing the powerful representation ability of deep learning models …