Large-scale spectrum occupancy learning via tensor decomposition and LSTM networks
A new paradigm for large-scale spectrum occupancy learning based on long short-term
memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum …
memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum …
Large-scale spectrum occupancy learning via tensor decomposition and lstm networks
A new paradigm for large-scale spectrum occupancy learning based on long short-term
memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum …
memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum …
Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM
The identification of spectrum opportunities is a pivotal requirement for efficient spectrum
utilization in cognitive radio systems. Spectrum prediction offers a convenient means for …
utilization in cognitive radio systems. Spectrum prediction offers a convenient means for …
TF2AN: A Temporal-Frequency Fusion Attention Network for Spectrum Energy Level Prediction
Modeling and predicting radio spectrum are significant for better understanding the behavior
of spectrum, managing their usage as well as optimizing the performance of dynamic …
of spectrum, managing their usage as well as optimizing the performance of dynamic …
Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models
In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently
use the spectrum. Spectrum occupancy prediction is a convenient way of revealing …
use the spectrum. Spectrum occupancy prediction is a convenient way of revealing …
Deep learning for spectrum prediction from spatial–temporal–spectral data
Spectrum prediction is challenging owing to its complex inherent dependency and
heterogeneity among the spectrum data. In this letter, we propose a novel end-to-end deep …
heterogeneity among the spectrum data. In this letter, we propose a novel end-to-end deep …
Soft fusion based cooperative spectrum prediction using LSTM
N Radhakrishnan, S Kandeepan, X Yu… - … Conference on Signal …, 2021 - ieeexplore.ieee.org
Spectrum prediction is an important solution proposed to efficiently manage the scarce
spectrum resource in various Dynamic Spectrum Access (DSA) applications. Deep Learning …
spectrum resource in various Dynamic Spectrum Access (DSA) applications. Deep Learning …
Convolutional LSTM-based long-term spectrum prediction for dynamic spectrum access
BS Shawel, DH Woldegebreal… - 2019 27th European …, 2019 - ieeexplore.ieee.org
The concept of Dynamic Spectrum Access (DSA) with Cognitive Radio (CR) as a key
enabler is considered as a promising solution to alleviate the inefficient use of the radio …
enabler is considered as a promising solution to alleviate the inefficient use of the radio …
Spectrum Prediction With Deep 3D Pyramid Vision Transformer Learning
In this paper, we propose a deep learning (DL)-based task-driven spectrum prediction
framework, named DeepSPred. The DeepSPred comprises a feature encoder and a task …
framework, named DeepSPred. The DeepSPred comprises a feature encoder and a task …
Performance analysis of long short-term memory-based Markovian spectrum prediction
N Radhakrishnan, S Kandeepan, X Yu… - IEEE Access, 2021 - ieeexplore.ieee.org
Dynamic Spectrum Access (DSA) solutions equipped with spectrum prediction can enable
proactive spectrum management and tackle the increasing demand for radio frequency (RF) …
proactive spectrum management and tackle the increasing demand for radio frequency (RF) …