Optimal Compressive Spectrum Sensing Based on Sparsity Order Estimation in Wideband Cognitive Radios

Luo, Yuan; Dang, Jiaojiao; Song, Zuxun

doi:10.1109/tvt.2019.2948966

Cited by 21 publications

(10 citation statements)

References 34 publications

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“…This algorithm requires the signal and noise power to be known a priori to tune the threshold parameter and is not suitable for estimating a higher sparsity order. By exploiting the autocorrelation and cross-correlation properties of the column vectors of the GSM, two-step adaptive compressive spectrum sensing (TS-ACSS) was proposed in [20]. In the first step, a coarse SOE was performed by identifying the slope change in the ordered arrangement of the inner product results of the column vectors of the sensing matrix with the obtained measurements.…”

Section: B Related Work On Soementioning

confidence: 99%

“…• The algorithm parameters depend on sparsity order [8]- [15], [21]. 2) They are computationally intensive [17], [20], [22].…”

Section: Motivationmentioning

confidence: 99%

“…By combining (19) and (20) with that of the measurement noise, each component y i of the BSM measurement vector y BSM is given as…”

Section: ) Statistics Of Bsm Measurementsmentioning

confidence: 99%

“…A simulation is performed to compare the SOEE performance of the KBSOE method with those of other SOE methods, such as Lopes [16], 2-GMM [17], trace [23], SPAMP [21], TS-ACSS [20], and RTCE [8] based methods.…”

Section: A Soee Performance Comparison Using a Synthetic Signalmentioning

confidence: 99%

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Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

et al. 2022

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We present a composite Compressed Sensing system for the acquisition and recovery of compressible signals, where a sparse Binary Sensing Matrix aids Sparsity Order Estimation, and a Gaussian Sensing Matrix aids reconstruction. The Binary Sensing Matrix is deterministic and is adapted according to the varying nature of the sparsity order. We estimate the sparsity order by exploiting the sparse structure of the Binary Sensing Matrix and the statistics of the obtained measurements. We refine the estimates of the sparsity order using a Kalman filter with a discrete Markov model that characterizes the sparsity order variation. A Binary Sensing Matrix-Aided Orthogonal Matching Pursuit is developed for faster recovery of compressible signals. Simulation results on real-world and synthetic data demonstrate the merits of the proposed sparsity order estimation and recovery methods compared to other existing methods. Our proposed methods are practical and recover compressible signals at least 25% faster than the existing methods.

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Section: B Related Work On Soementioning

confidence: 99%

“…• The algorithm parameters depend on sparsity order [8]- [15], [21]. 2) They are computationally intensive [17], [20], [22].…”

Section: Motivationmentioning

confidence: 99%

“…By combining (19) and (20) with that of the measurement noise, each component y i of the BSM measurement vector y BSM is given as…”

Section: ) Statistics Of Bsm Measurementsmentioning

confidence: 99%

Section: A Soee Performance Comparison Using a Synthetic Signalmentioning

confidence: 99%

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Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

et al. 2022

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“…Thus, researchers have come up with an idea to utilize the unused spectrum through smart sensing of available spectrum through various technologies such as cognitive radio (CR). The CR can effectively deal with the spectrum scarcity problems also; it permits secondary users (SUs) to use the unoccupied spectrum without affecting the transmission of primary users (PUs) [3]- [4]. In the meantime, the Federal Communication Commission (FCC) investigated the spectrum occupancy and found that a major portion of the available spectrum is unused due to the static allocation technique [5] [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Spectrum Occupancy Measurement of Cellular Spectrum and Smart Network Sharing in Pakistan

Mirani¹,

Memon²,

Hussain³

et al. 2020

IJACSA

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In wireless communication, the radio spectrum is a very rare and precious resource that has currently become a major problem to efficiently exploit the underutilized band of the static allocated licensed band. Recently, the cognitive radio (CR) has emerged as a promising technology to overcome the spectrum crisis, in which, the licensed band can be utilized by the unlicensed user until and unless it does not affect the transmission of the licensed band. In this paper, the spectrum occupancy of three bands i.e. GSM 900, 1800 and 2100 bands have been measured through spectrum analyzer in the indoor and outdoor environment. The measured results of all the three bands have been calculated through MATLAB against the power spectral density versus frequency plots. Results have shown that the majority of the licensed band is underutilized. Therefore, the CR can play a pivotal role to efficiently utilize the unused spectrum and to overcome the cellular wireless spectrum crisis in Pakistan. The second part of this paper deals with the emerging concept of network sharing among mobile operators and its impacts on cost. Network sharing become the standard among mobile operators worldwide and so in Pakistan. Capital (CAPEX) as well as operational (OPEX) expenditure and rapid advancement in technology encouraged all operators to go for sharing business models. In Pakistan, all four mobile operators Jazz, Telenor, Zong, and Ufone are actively adopting this model to maintain EBITDA (earnings before interest, taxes, depreciation, and amortization). Mainly there are two types of network sharing, Passive infrastructure sharing, and active resource sharing. Passive network sharing is widely used in Pakistan among operators.

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Enhanced Cooperative Compressive Spectrum Sensing in Cognitive Radio Networks

Benzater,

Teguig,

Lassami

2024

Trans Emerging Tel Tech

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The demand for bandwidth in cognitive radio networks (CRNs) is growing as applications become increasingly data intensive. Techniques such as compressed spectrum sensing (CSS) and cooperative spectrum sensing (C‐SS) are employed to address this challenge. C‐SS enhances overall detection accuracy and reliability by enabling multiple nodes to share and combine their local sensing data. Conversely, CSS effectively reduces the required information for spectrum usage decision making, thereby improving bandwidth utilization. Integrating these two methods allows CRNs to utilize the spectrum reliably and efficiently, leading to increased spectral efficiency. To further improve reconstruction performance, we leverage the sparsity concept to transcend hardware constraints and merge restrictions from both real and synthesized channels. This approach involves the virtual synthesis of channels, which linearly enhances the signal‐to‐noise ratio (SNR) within the network's size range. Simulation results demonstrate that our proposed method offers significant advantages over single‐node recovery, as validated by simulations and software defined radio (SDR) Implementation. The integration of spectral estimations from various local CR detectors enhances spatial diversity gain and sensing quality, particularly in fading channels. Compared to traditional approaches, our method achieves superior performance, evidenced by an increase in (from 93.97% to 96.52%) with almost the same .

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Optimal Compressive Spectrum Sensing Based on Sparsity Order Estimation in Wideband Cognitive Radios

Cited by 21 publications

References 34 publications

Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

Spectrum Occupancy Measurement of Cellular Spectrum and Smart Network Sharing in Pakistan

Enhanced Cooperative Compressive Spectrum Sensing in Cognitive Radio Networks

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