Random sampling for effective spectrum sensing in cognitive radio time slotted environment

Serrano, Salvatore; Scarpa, Marco; Maali, Asmaa; Soulmani, Abdallah; Boumaaz, Najib

doi:10.1016/j.phycom.2021.101482

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…Design the single communication spectrum access scheme. In order to balance the traffic of the macro cell and reduce the peak traffic of the macro cell, this paper considers introducing D2D communication, transferring the macro cell flow to the micro cell through relay assistance, and making reasonable use of the spectrum resources [6]. Analyze D2D users and spectrum access schemes.…”

Section: Single Communication Spectrum Access Of High-speed Railway A...mentioning

confidence: 99%

Dynamic allocation of communication spectrum resources in high-speed rail automatic control system based on mobile edge computing

Yang

Zhu

2023

Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023)

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The transmission success rate of the traditional dynamic allocation method of communication spectrum resources is easily affected by the environment. Therefore, mobile edge computing technology is introduced to conduct a comprehensive study on the dynamic allocation of communication spectrum resources of high-speed railway automatic control system, establish the communication model of automatic control system, and realize the dynamic allocation of communication spectrum resources based on mobile edge computing, realizing the frequency compatibility between networks and efficient allocation of spectrum resources. The test results show that the link transmission success rate of this method can reach more than 97%.

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Section: Single Communication Spectrum Access Of High-speed Railway A...mentioning

confidence: 99%

Dynamic allocation of communication spectrum resources in high-speed rail automatic control system based on mobile edge computing

Yang

Zhu

2023

Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023)

View full text Add to dashboard Cite

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“…Traditional SS algorithms cannot adequately adapt to these challenges, leading to inaccurate or delayed spectrum occupancy detection. Moreover, they usually fail to take full advantage of the temporal, frequency or spatial dependencies that exist in the detected signals, resulting in rather limited performance [10][11][12]. Machine learning (ML) techniques have demonstrated significant potential in improving the performance of spectrum sensing.…”

Section: Introductionmentioning

confidence: 99%

1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing

Serghini,

Semlali,

Maali

et al. 2023

Future Internet

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Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted considerable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduce multiple cooperative spectrum sensing schemes based on a deep neural network, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity patterns of the primary user. The scenario of an imperfect transmission channel is considered for service messages to demonstrate the robustness of the proposed model. The performance of the proposed methods is evaluated with the receiver operating characteristic curve, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory-based method, surpassing the performance of the other proposed schemes and the current state-of-the-art methods in terms of detection probability, while ensuring a reasonable online detection time.

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Real time implementation of SDR-based RF source detection and localization in restricted area

Slimeni,

Delleji,

Agrebi

et al. 2023

Telecommun Syst

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Radio frequency (RF) detection and source localisation is a critical requirement for military and civilian applications. This paper implements a multistep RF detection technique in a software defined radio (SDR) platform, that starts by a fast energy detection then go through cyclostationarity feature detection to deal with low detected signals. This combination offers range potential to monitor wider area. The system relies on the received signal strength indication (RSSI) measurements to estimate the distance between the sensing system and the detected RF source. General-purpose SDR platforms offer more flexibility and cost efficiency, compared to dedicated localisation systems. It enables providing an embedded standalone system able to detect RF signals and to estimate the polar coordinates of the emitting source. Through experiments, we prove the validity of the proposed system for real applications.

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Random sampling for effective spectrum sensing in cognitive radio time slotted environment

Cited by 11 publications

References 18 publications

Dynamic allocation of communication spectrum resources in high-speed rail automatic control system based on mobile edge computing

Dynamic allocation of communication spectrum resources in high-speed rail automatic control system based on mobile edge computing

1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing

Real time implementation of SDR-based RF source detection and localization in restricted area

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