A Machine Learning Approach for Simultaneous Demapping of QAM and APSK Constellations
A Gansekoele, A Balatsoukas-Stimming… - arXiv preprint arXiv …, 2024 - arxiv.org
As telecommunication systems evolve to meet increasing demands, integrating deep neural
networks (DNNs) has shown promise in enhancing performance. However, the trade-off …
networks (DNNs) has shown promise in enhancing performance. However, the trade-off …
Evaluation of Neural Demappers for Trainable Constellation in an End-to-End Communication System
Conventional M-ary Quadrature Amplitude Modulation (M-QAM) constellation designs such
as rectangular constellation, are based on mathematical data and estimated channel …
as rectangular constellation, are based on mathematical data and estimated channel …
Unified Deep Neural Demodulation Network Design for QAM Signal Recovery
B Xiao, S Zheng, J Zhu, Z Zhang… - 2023 IEEE 98th …, 2023 - ieeexplore.ieee.org
In this paper, we focus on designing an unified deep neural demodulation network for
recovering multiple QAM signals, which can adapt to the adaptive QAM modulation signal …
recovering multiple QAM signals, which can adapt to the adaptive QAM modulation signal …
Probabilistic constellation shaping with denoising diffusion probabilistic models: A novel approach
With the incredible results achieved from generative pre-trained transformers (GPT) and
diffusion models, generative AI (GenAI) is envisioned to yield remarkable breakthroughs in …
diffusion models, generative AI (GenAI) is envisioned to yield remarkable breakthroughs in …
Combining deep learning and linear processing for modulation classification and symbol decoding
Deep learning has been recently applied to many problems in wireless communications
including modulation classification and symbol decoding. Many of the existing end-to-end …
including modulation classification and symbol decoding. Many of the existing end-to-end …
Demodulation of faded wireless signals using deep convolutional neural networks
AS Mohammad, N Reddy, F James… - 2018 IEEE 8th Annual …, 2018 - ieeexplore.ieee.org
This paper demonstrates exceptional performance of approximately 10.0 dB learning-based
gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh …
gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh …
Deep learning-based demodulation of radio signal
K Chia, VM Baskaran - 2022 International Symposium on …, 2022 - ieeexplore.ieee.org
M-ary quadrature amplitude modulation (M-QAM) modulated signal is commonly used in
digital telecommunication systems for its arbitrarily high spectral efficiencies limited only by …
digital telecommunication systems for its arbitrarily high spectral efficiencies limited only by …
Constellation Shaping for Phase Noise Channels with Deep Learning Approach
Constellation shaping can significantly enhance the capacity of communication systems
without incurring extra bandwidth. The statistics of the noise in the system dictate the …
without incurring extra bandwidth. The statistics of the noise in the system dictate the …
Joint learning of geometric and probabilistic constellation shaping
The choice of constellations largely affects the performance of communication systems.
When designing constellations, both the locations and probability of occurrence of the points …
When designing constellations, both the locations and probability of occurrence of the points …
Recurrent network with attention for symbol detection in communication systems
K Chia, VM Baskaran, KS Wong… - … on Intelligent Signal …, 2022 - ieeexplore.ieee.org
One major challenge for wireless receivers to maintain information fidelity involves the
demodulation of faded signals in noisy environments. Typical demodulation techniques for …
demodulation of faded signals in noisy environments. Typical demodulation techniques for …