Search Results for author:
Found 12 papers, 8 papers with code
Explainable AI for Enhancing Efficiency of DL-based Channel Estimation
In such applications, using AI as black-box models is risky and challenging.
Extending CAM-based XAI methods for Remote Sensing Imagery Segmentation
This paper offers to bridge this gap by adapting the recent XAI classification algorithms and making them usable for muti-class image segmentation, where we mainly focus on buildings' segmentation from high-resolution satellite images.
Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentation
To measure the performance of the Sobol method for segmentation, we propose a quantitative XAI evaluation method based on a learnable noise model.
Towards Explainable AI for Channel Estimation in Wireless Communications
This manuscript proposes a novel XAI-based channel estimation (XAI-CHEST) scheme that provides detailed reasonable interpretability of the deep learning (DL) models that are employed in doubly-selective channel estimation.
Deep Learning Based Channel Estimation in High Mobility Communications Using Bi-RNN Networks
Doubly-selective channel estimation represents a key element in ensuring communication reliability in wireless systems.
A Survey on Deep Learning based Channel Estimation in Doubly Dispersive Environments
Wireless communications systems are impacted by multi-path fading and Doppler shift in dynamic environments, where the channel becomes doubly-dispersive and its estimation becomes an arduous task.
Low Complex Methods for Robust Channel Estimation in Doubly Dispersive Environments
Wireless communications play a significant role in facilitating several mobile applications like unmanned aerial vehicles, high-speed railway, and vehicular communications.
CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications
IEEE 802. 11p standard defines wireless technology protocols that enable vehicular transportation and manage traffic efficiency.
Joint TRFI and Deep Learning for Vehicular Channel Estimation
IEEE 802. 11p standard enables the wireless technology that defines vehicular communications.
Adaptive Channel Estimation based on Deep Learning
Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization.
Enhancing Least Square Channel Estimation Using Deep Learning
Least square (LS) channel estimation employed in various communications systems suffers from performance degradation especially in low signal-to-noise ratio (SNR) regions.
Deep Learning Based Channel Estimation Schemes for IEEE 802.11p Standard
IEEE 802. 11p standard is specially developed to define vehicular communications requirements and support cooperative intelligent transport systems.
