RFML-Driven Spectrum Prediction: A Novel Model-Enabled Autoregressive Network
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3190691
Abstract: Spectrum prediction is of crucial importance for realizing the cognitive Internet of Things to tackle the spectrum scarcity problem. Deep-learning-based spectrum prediction methods have attracted extensive attention due to their superior accuracy. However, the training… read more here.
Keywords: network; driven spectrum; prediction; scheme ... See more keywords
Deep Learning for Spectrum Prediction From Spatial–Temporal–Spectral Data
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2020.3045205
Abstract: 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-learning-based model, entitled spatial-temporal-spectral prediction network (STS-PredNet), to collectively predict the… read more here.
Keywords: spectrum prediction; spatial temporal; spectrum data; prediction ... See more keywords
An Improved Initialization Method for Fast Learning in Long Short-Term Memory-Based Markovian Spectrum Prediction
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Cognitive Communications and Networking"
DOI: 10.1109/tccn.2020.3046330
Abstract: The opportunistic sharing of frequency bands supported in the Dynamic Spectrum Access (DSA) paradigm resolves the spectrum scarcity issue in wireless communications. To this end, deep learning models such as Long Short-Term Memory (LSTM) are… read more here.
Keywords: long short; method; spectrum; initialization ... See more keywords
Boost Spectrum Prediction With Temporal-Frequency Fusion Network via Transfer Learning
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Mobile Computing"
DOI: 10.1109/tmc.2021.3136941
Abstract: Modeling and predicting the radio spectrum is vital for spectrum management, such as spectrum sharing and anomaly detection. Nevertheless, the precise spectrum prediction is challenging due to the interference from both intra-spectrum and external factors.… read more here.
Keywords: mml; tex math; inline formula; spectrum prediction ... See more keywords
Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks
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DOI: 10.1364/prj.7.000368
Abstract: In this article, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design and performance optimization for the plasmonic waveguide coupled with cavities structure (PWCCS) based on artificial neural networks (ANNs). The… read more here.
Keywords: based artificial; spectrum prediction; plasmonic waveguide; inverse design ... See more keywords
Cross-band spectrum prediction based on deep transfer learning
Sign Up to like & getrecommendations! Published in 2020 at "China Communications"
DOI: 10.23919/jcc.2020.02.006
Abstract: Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data. In practice, for a given spectrum band of interest, when facing relatively scarce historical data, spectrum… read more here.
Keywords: transfer learning; band; spectrum; spectrum prediction ... See more keywords