A novel approach to WaveNet architecture for RF signal separation with learnable dilation and data augmentation
Y Tian, A Alhammadi, A Quran… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
In this paper, we address the intricate issue of RF signal separation by presenting a novel
adaptation of the WaveNet architecture that introduces learnable dilation parameters …
adaptation of the WaveNet architecture that introduces learnable dilation parameters …
DEMUCS for data-driven RF signal denoising
In this paper, we present our radio frequency signal denoising approach, RFDEMUCS, 1 for
the 2024 IEEE ICASSP RF Signal Separation Challenge. Our approach is based on the DE …
the 2024 IEEE ICASSP RF Signal Separation Challenge. Our approach is based on the DE …
High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning
Co-channel interference cancellation (CCI) is the process used to reduce interference from
other signals using the same frequency channel, thereby enhancing the performance of …
other signals using the same frequency channel, thereby enhancing the performance of …
Radar Signal Recognition through Self-Supervised Learning and Domain Adaptation
Automatic radar signal recognition (RSR) plays a pivotal role in electronic warfare (EW), as
accurately classifying radar signals is critical for informing decision-making processes …
accurately classifying radar signals is critical for informing decision-making processes …
[PDF][PDF] A U-Net architecture for time-frequency interference signal separation of RF waveforms
This paper presents a data-driven approach to solve the challenge of separating co-channel
mixture signals in the radio spectrum. The main aim is to extract the signal-of-interest with …
mixture signals in the radio spectrum. The main aim is to extract the signal-of-interest with …
RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge
This paper addresses the critical problem of interference rejection in radio-frequency (RF)
signals using a novel, data-driven approach that leverages state-of-the-art AI models …
signals using a novel, data-driven approach that leverages state-of-the-art AI models …
[PDF][PDF] Signal separation in radio spectrum using self-attention mechanism
For a radio frequency (RF) signal separation task, we propose two models operating directly
on the time-domain waveform: a Transformer U-Net, a convolution-attention based model …
on the time-domain waveform: a Transformer U-Net, a convolution-attention based model …
[PDF][PDF] Improving data-driven RF signal separation with SOI-matched autoencoders
L Henneke - Proc. IEEE Int. Conf. Acoust., Speech, Signal …, 2024 - rfchallenge.mit.edu
While the use of deep learning-based methods in radio frequency (RF) signal processing
has steadily increased in recent years, little attention was paid to RF signal separation …
has steadily increased in recent years, little attention was paid to RF signal separation …
[PDF][PDF] OPTIMIZED SIZE-PERFORMANCE MODEL FOR INTERFERENCE REJECTION IN DIGITAL COMMUNICATIONS FOR THE ICASSP 2024 CHALLENGE
AJ Fesharaki, HA Piralidehi, AH Karkan… - rfchallenge.mit.edu
This study summarizes our submitted model to the ICASSP 2024 interference extraction
challenge. In this challenge, the goal is to extract the Signal of Interest (SOI) from the mixture …
challenge. In this challenge, the goal is to extract the Signal of Interest (SOI) from the mixture …