Accurate Spectrum Map Construction for Spectrum Management Through Intelligent Frequency-Spatial Reasoning

Zhou, Fuhui; Wang, Chenyue; Wu, Guangyu; Wu, Yuhang; Wu, Qihui; Al-Dhahir, Naofal

doi:10.1109/tcomm.2023.3267569

Cited by 8 publications

(6 citation statements)

References 46 publications

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“…In contrast, the bulk of past endeavours have mostly utilized frequency correlation as a means of generating spectrum maps. However, it is important to note that this approach does not provide a guarantee of correctness in the absence of substantial data from the target [1,21,22].…”

Section: Optimization For Prediction Algorithmsmentioning

confidence: 99%

“…The current joint frequency-spatial correlation-based systems that just employ frequency correlation without considering how differently frequencies fade throughout propagation. Two precise approaches for creating spectrum maps are put forward in [1] utilizing various frequency-spatial reasoning techniques. Also, to make the full use of frequency and spatial correlations completely and properly generate the target spectrum map, in [1], a model for representing joint frequency-spatial spectra is established.…”

Section: Optimization For Prediction Algorithmsmentioning

confidence: 99%

“…Two precise approaches for creating spectrum maps are put forward in [1] utilizing various frequency-spatial reasoning techniques. Also, to make the full use of frequency and spatial correlations completely and properly generate the target spectrum map, in [1], a model for representing joint frequency-spatial spectra is established. After that, two neural networks, namely a novel Conditional Generative Adversarial Network (CGAN) and a novel autoencoder, are constructed with the purpose of introducing two intelligent frequency-spatial reasoning approaches [1].…”

Section: Optimization For Prediction Algorithmsmentioning

confidence: 99%

“…Also, to make the full use of frequency and spatial correlations completely and properly generate the target spectrum map, in [1], a model for representing joint frequency-spatial spectra is established. After that, two neural networks, namely a novel Conditional Generative Adversarial Network (CGAN) and a novel autoencoder, are constructed with the purpose of introducing two intelligent frequency-spatial reasoning approaches [1]. As for the method based on the novel autoencoder, the steps are shown in Figure 3.…”

Section: Optimization For Prediction Algorithmsmentioning

confidence: 99%

“…With the rapid proliferation of intelligent terminals and the variety of ultra-wideband applications, the sixth generation (6G) wireless communication networks are facing significant challenges in spectrum mapping [1][2][3]. A spectrum map is a representation of the spatial distribution of the received signal intensity by projecting the received signal strength to the appropriate geographic coordinates in an area of interest.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Spectrum map construction optimisation schemes: Sampling and prediction

2024

ACE

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The proliferation of electromagnetic devices presents a significant challenge in developing effective techniques for spectrum monitoring, management, and security. The utilization of spectrum cartography has been acknowledged as a viable approach to address the aforementioned difficulties. This latter presents a variety of techniques aimed at enhancing the efficiency of the current spectrum mapping methodology. The subject matter can be categorized into two primary components, namely sampling and spectrum prediction. Sampling part includes methods to find the most valuable sampling points and methods of sampling hardware optimization. Spectrum prediction includes algorithms utilizing frequency-spatial reasoning techniques to estimate the target spectrum map by data from the nearby area, and algorithms utilizing ROSMP framework to estimate the spectrum map from past data. The introduction of techniques is divided into the 2 types, together with key algorithms and devices used in each method. Additionally, the letter lists some drawbacks of certain methods and discuss their development prospects.

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Section: Optimization For Prediction Algorithmsmentioning

confidence: 99%

Section: Optimization For Prediction Algorithmsmentioning

confidence: 99%

Section: Optimization For Prediction Algorithmsmentioning

confidence: 99%

Section: Optimization For Prediction Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spectrum map construction optimisation schemes: Sampling and prediction

2024

ACE

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Radio Map Reconstruction With Adaptive Spatial Feature Learning

Yang,

Guo

2024

IET Signal Processing

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Radio map reconstruction is a fundamental problem of great relevance in numerous real‐world applications, such as network planning and fingerprint localization. Sampling the complete radio map is prohibitively costly in practice and difficult to achieve. Such methods for reconstructing radio maps from a subset of measurements are now gaining additional attention. In this paper, we first explore the spatial features of signals on the radio map and formulate the reconstruction problem as an optimization problem with feature penalties. Then, we propose an iteration algorithm with spatial feature learning to reconstruct signals on the radio map, which improves the reconstruction accuracy by using an adaptive feature dictionary. Numerical examples are given to demonstrate the viability and performance of our method at last.

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Fine‐grained spectrum map inference: A novel approach based on deep residual network

He,

Zhu,

Wang

et al. 2024

IET Communications

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Spectrum map is a database that stores multidimensional representations of spectrum situation information. It provides support for spectrum sensing and endows wireless communication networks with intelligence. However, the ubiquitous deployment of monitoring devices leads to huge costs of operation and maintenance. It indicates that an approach is needed to reduce the number of monitoring devices, but prevent the degradation of data granularity. Therefore, this paper focuses on the accurate construction of the spectrum map. It aims to infer the fine‐grained spectrum situation of the target region based on coarse‐grained observation. In order to solve this problem, an inference framework based on deep residual network is developed in this paper. In the case of rule deployment for sensing nodes, it adopts the idea of super resolution to improve the accuracy of the spectrum map. The framework is composed of two major parts: an inference network, which generates fine‐grained spectrum maps from coarse‐grained counterparts by using feature extraction module and upsampling construction module; and a fusion network, which considers the influence of environmental factors to further improve the performance. A large number of experiments on simulated datasets verify the effectiveness of the proposed method.

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Accurate Spectrum Map Construction for Spectrum Management Through Intelligent Frequency-Spatial Reasoning

Cited by 8 publications

References 46 publications

Spectrum map construction optimisation schemes: Sampling and prediction

Spectrum map construction optimisation schemes: Sampling and prediction

Radio Map Reconstruction With Adaptive Spatial Feature Learning

Fine‐grained spectrum map inference: A novel approach based on deep residual network

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