International Conference on Computer and Knowledge Engineering

Home / 14th International Conference on Computer and Knowledge Engineering

Adaptive-A-GCRNN: Enhancing Real-time Multi-band Spectrum Prediction through Attention-based Spatial-Temporal Modeling

Authors :

Seyed majid Hosseini¹ Seyedeh Mozhgan Rahmatinia² Seyed Amin Hosseini Seno³ Hadi Sadoghi yazdi⁴

1- Ferdowsi university of mashhad 2- Ferdowsi university of mashhad 3- Ferdowsi university of mashhad 4- Ferdowsi university of mashhad

Keywords :

Multi-band spectrum prediction،Spatial-temporal Feature Extraction،Deep learning،Graph neural networks،Attention mechanism

Abstract :

— Multi-band spectrum prediction is a crucial task for spectrum management and improving spectrum utilization. Despite the complexity and spatiotemporal variability of spectrum data, which make accurate prediction challenging, leveraging spatial and temporal features of spectra can significantly enhance prediction accuracy. In this paper, we propose a hybrid deep learning model for multi-band spectrum prediction. Our model incorporates an Adaptive-GCN approach to learn spatial dependencies among spectra, as well as a GRU to extract temporal features. Additionally, we employ an attention mechanism at the output of the GRU to enhance the model's ability to capture long-term temporal dependencies. The proposed model's adjacency matrix is learnable, enabling an adaptive graph model without requiring the entire training dataset. This not only improves the model's adaptability but also allows for near real-time applications. We compared our model with the A-GCRNN on a real-world spectrum dataset, and the results demonstrated improved accuracy and flexibility of the proposed model.

Papers List

List of archived papers

A Stacking Ensemble Framework for Ransomware Detection on the Bitcoin Blockchain Using Transaction Graph Analytics

Mohammad Mobin Teymourpour - Parsa Hedayatnia - Mohammad Allahbakhsh - Haleh Amintoosi

AvashoG2P: A multi-module G2P Converter for Persian

Ali Moghadaszadeh - Fatemeh Pasban - Mohsen Mahmoudzadeh - Maryam Vatanparast - Amirmohammad Salehoof

Camouflage Object Segmentation with Attention-Guided Pix2Pix and Boundary Awareness

Erfan Akbarnezhad Sany - Fatemeh Naserizadeh - Parsa Sinichi - Seyyed Abed Hosseini

Extracting Major Topics of COVID-19 Related Tweets

Faezeh Azizi - Hamed Vahdat-Nejad - Hamideh Hajiabadi - Mohammad Hossein Khosravi

A Weighted TF-IDF-based Approach for Authorship Attribution

Ali Abedzadeh - Reza Ramezani - Afsaneh Fatemi

Atlas-based segmentation of cardiac chambers in systolic and diastolic phases of echocardiographic images

Elham Fathipour - Mahdi Saadatmand

A Novel Approach for Image-Text Matching Cross-Modal Space Learning

Amirreza Ebrahimi - Mohammad Javad Parseh - Pejman Rasti

Bipartite link prediction improvement using the effective utilization of edge betweenness centrality

Sadegh Sulaimany Sulaimany - Yasin Amini

Deep Learning-Based Malaysian Sign Language (MSL) Recognition: Exploring the Impact of Color Spaces

Ervin Gubin Moung - Precilla Fiona Suwek - Maisarah Mohd Sufian - Valentino Liaw - Ali Farzamnia - Wei Leong Khong

XAI for Transparent Autonomous Vehicles: A New Approach to Understanding Decision-Making in Self-driving Cars

Maryam Sadat Hosseini Azad - Amir Abbas Hamidi Imani - Shahriar Baradaran Shokouhi