Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology

Molina-Tenorio, Yanqueleth; Prieto‐Guerrero, Alfonso; Aguilar-Gonzalez, Rafael

doi:10.3390/s21103506

Cited by 16 publications

(8 citation statements)

References 38 publications

(49 reference statements)

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“…Hence, several comparative studies have been carried out in order to measure the performance of the proposed MBSS technique compared with other classical methods, such as energy detectors. The efficiency of locating the PUs by this MBSS technique stands out in SNR values close to 0 dB [11]. Table 1 highlights the principal contributions of these previous works.…”

Section: Preliminary Workmentioning

confidence: 90%

“…Many MBSS techniques issued from digital signal processing and machine learning (ML), such as wavelets, compressed sensing, energy detectors, and blind or semiblind methods, have been proposed, primarily in simulated scenarios [4][5][6][7][8][9][10]. Other works have been implemented in a real wireless communications environment [11][12][13][14][15][16], where softwaredefined radio (SDR) and universal software radio peripheral (USRP) technologies have been employed. Recently, SDR devices such as the HackRF One, the LimeSDR Mini, and the RTL-SDR have become extremely popular because of their affordability and good performances [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

Molina-Tenorio

Prieto‐Guerrero

Aguilar-Gonzalez

et al. 2023

Sensors

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Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth.

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Section: Preliminary Workmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

Molina-Tenorio

Prieto‐Guerrero

Aguilar-Gonzalez

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The work presented in Reference 11 mainly considers the different coding and modulation techniques for reducing the power consumption. In Reference 12, a framework is proposed to calculate the energy consumption in the spectrum sensing process. They have divided the spectrum into various frequency bands.…”

Section: Related Workmentioning

confidence: 99%

Cooperation and energy harvesting based spectrum sensing schemes for green cognitive radio networks

Srivastava

Kaur

2022

Trans Emerging Tel Tech

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Cognitive radio network (CRNs) with energy harvesting approach to identify unused spectrum bands and utilize them opportunistically is observed as an evolving technology to handle spectrum scarcity problems. In CRNs, the secondary users (SUs) continuously sense the primary user's (PUs) spectrum to secure the data transmission of PUs and to access the vacant PU's spectrum for their data transmission. For designing future green networks, it is essential that the spectrum sensing process must be accurate, rapid, and energy‐efficient. Therefore, in this work, three sensing schemes are proposed. The first is a cooperation‐based joint cooperation spectrum sensing (JCSS) scheme in which the cooperative CRNs permit the SUs, working in an identical band, to cooperate, and this cooperation approach decreases sensing time and improves sensing probability for vacant spectrum. The result shows that the sensing probability for a vacant spectrum is increased by 34%. The proposed JCSS scheme considers the inherent asymmetry property of the network; therefore, it helps to improve the overall rapidity gain of the networks. The other two proposed sensing schemes, preset spectrum sensing (PSS) and viscous spectrum sensing (VSS), are based on energy harvesting that analyzes the percentage of energy‐saving for various states. The suggested schemes are better in terms of scalability because, in these schemes, it is not essential to sense all spectrums, which leads to a reduction in sensing time.

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“…The multi-band spectrum sensing technique proposed in [24] is based on the idea of linking several affordable SDRs to operate in parallel on different frequency segments. In the paper, a sensor for spectrum occupancy detection is proposed, enhanced by advanced software-implemented signal processing techniques.…”

Section: B Sdr-based Solutionsmentioning

confidence: 99%

Hardware-Accelerated Real-Time Spectrum Analyzer With a Broadband Fast Sweep Feature Based on the Cost-Effective SDR Platform

Flak

2022

IEEE Access

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Radio Frequency (RF) spectrum monitoring and broadband signal analysis have multiple application areas, especially in the era of a constantly growing number of wireless devices. One of the essential challenges for a spectrum sensor is to achieve an adequate measurement rate over a wide bandwidth to detect signals of short duration so that a low latency response can be provided. In procedures that require field measurements, and some compromise in accuracy is acceptable, low-cost Software Defined Radio (SDR) devices can be used instead of expensive and bulky professional spectrum analyzers. This paper introduces a real-time swept spectrum sensor based on LimeSDR-USB with custom embedded Field Programmable Gate Array (FPGA) firmware, designed to outperform similar software implementations. The Welch's spectral density estimation is implemented in hardware to minimise the USB transfer rate and offload the host PC signal processing. Furthermore, the frequency tuning state machine and cache calibration memory are also managed by the FPGA to reduce the blind time during broadband sweep. The performance of the proposed solution indicates up to 96 MHz of real-time bandwidth along with a capability of less than millisecond cumulative sweep time per gigahertz. The characteristics of various design elements are investigated and refined during simulation and laboratory measurements, whereas the final prototype implementation is verified in real-world scenarios. The results demonstrate the effectiveness of the proposed device as a sensor for propagation studies, multiband spectrum utilisation monitoring, and spectral white spaces detection.INDEX TERMS Electromagnetic analysis, field programmable gate array, radio frequency, software defined radio, surveillance.

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Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology

Cited by 16 publications

References 38 publications

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

Cooperation and energy harvesting based spectrum sensing schemes for green cognitive radio networks

Hardware-Accelerated Real-Time Spectrum Analyzer With a Broadband Fast Sweep Feature Based on the Cost-Effective SDR Platform

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