Spectrum Sensing Methods and Their Performance

Tellambura, Chintha

doi:10.1007/978-981-10-1389-8_6-1

Cited by 6 publications

(10 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance analysis of the ED method using receiver operating characteristic (ROC) curves for various fading channels is detailed in [39][40][41][42]. In [39], the SLC diversity reception, also known as the soft decision fusion scheme, was evaluated with a focus on the Additive White Gaussian Noise (AWGN), Rayleigh, Rician, Nakagami, Weibull, and Hoyt fading channels.…”

Section: Related Work On the Spectrum Sensing Of Ofdm Signals Based On The Ed Methodsmentioning

confidence: 99%

“…A tutorial presenting different ED methods and the basic parameters of the classical energy detector in SISO transmission systems is given in [41]. For ED based on two SL diversity schemes (SLS and SLC), the mathematical formulation of false alarm and detection probability in MISO transmission systems is given.…”

Section: Related Work On the Spectrum Sensing Of Ofdm Signals Based On The Ed Methodsmentioning

confidence: 99%

“…The previous research showed that the implementation of MIMO transmissions has an impact on the ED process [9,27,[36][37][38][39][40][41][42][43]. Although a number of issues has been considered in related works, a comprehensive analysis dedicated to the ED performance based on SLC technique in MIMO-OFDM systems is still missing.…”

Section: Related Work On the Spectrum Sensing Of Ofdm Signals Based On The Ed Methodsmentioning

confidence: 99%

“…where Λ r represents the test statistics of the r-th Rx branch of the SU device. It was shown in [32,41] that Λ r has a demanding distribution complexity. It involves non-central, chi-square distribution, which can be represented as a sum of the 2N squares of the independent and non-identically distributed (i.n.i.d.)…”

Section: Energy Detectionmentioning

confidence: 99%

See 3 more Smart Citations

Performance Analyses of Energy Detection Based on Square-Law Combining in MIMO-OFDM Cognitive Radio Networks

Lörincz

Ramljak²,

Begušić

2021

Sensors

View full text Add to dashboard Cite

Cognitive radio (CR) technology has the potential to detect and share the unutilized spectrum by enabling dynamic spectrum access. To detect the primary users’ (PUs) activity, energy detection (ED) is widely exploited due to its applicability when it comes to sensing a large range of PU signals, low computation complexity, and implementation costs. As orthogonal frequency-division multiplexing (OFDM) transmission has been proven to have a high resistance to interference, the ED of OFDM signals has become an important local spectrum-sensing (SS) concept in cognitive radio networks (CRNs). In combination with multiple-input multiple-output (MIMO) transmissions, MIMO-OFDM-based transmissions have started to become a widely accepted air interface, which ensures a significant improvement in spectral efficiency. Taking into account the future massive implementation of MIMO-OFDM systems in the fifth and sixth generation of mobile networks, this work introduces a mathematical formulation of expressions that enable the analysis of ED performance based on the square-law combining (SLC) method in MIMO-OFDM systems. The analysis of the ED performance was done through simulations performed using the developed algorithms that enable the performance analysis of the ED process based on the SLC in the MIMO-OFDM systems having a different number of transmit (Tx) and receive (Rx) communication branches. The impact of the distinct factors including the PU Tx power, the false alarm probability, the number of Tx and Rx MIMO branches, the number of samples in the ED process, and the different modulation techniques on the ED performance in environments with different levels of signal-to-noise ratios are presented. A comprehensive analysis of the obtained results indicated how the appropriate selection of the analyzed factors can be used to enhance the ED performance of MIMO-OFDM-based CRNs.

show abstract

Section: Related Work On the Spectrum Sensing Of Ofdm Signals Based On The Ed Methodsmentioning

confidence: 99%

Section: Related Work On the Spectrum Sensing Of Ofdm Signals Based On The Ed Methodsmentioning

confidence: 99%

Section: Related Work On the Spectrum Sensing Of Ofdm Signals Based On The Ed Methodsmentioning

confidence: 99%

Section: Energy Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Analyses of Energy Detection Based on Square-Law Combining in MIMO-OFDM Cognitive Radio Networks

Lörincz

Ramljak²,

Begušić

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Waveform detection uses the known signal patterns to detect PU signals. It does not require any silence period of SUs [8]. The cyclic prefix autocorrelation detection can provide accurate detection of orthogonal frequency-division multiplexing (OFDM) based PU signals [9].…”

Section: Introductionmentioning

confidence: 99%

Symmetric Peaks-Based Spectrum Sensing Algorithm for Detecting Modulated Signals

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Cognitive radio is considered an effective solution to spectrum shortages, which has been a significant issue in the next generation of wireless communications. In this paper, we focus on spectrum sensing under a low signal-to-noise-ratio (SNR) and noise fluctuation and propose a symmetric peaksbased spectrum sensing algorithm for modulated signals. First, we analyze the characteristics of the cyclic autocorrelation function of modulated signals, and construct a detection domain for detecting primary users based on the characteristics of the cyclic autocorrelation function of primary signals. Then, we introduce the significance level factor into the spectrum sensing, and develop a symmetric peaks criterion. Following this criterion, we propose a symmetric peaks-based spectrum sensing algorithm. Finally, we give the probabilities of detection and false alarm of the spectrum sensing algorithm, discuss the effect of the significance level factor on the spectrum sensing performance, and compare the complexity of the algorithm with that of other algorithms. The spectrum sensing algorithm proposed does not require any prior knowledge of primary user signals or noise in the systems, and can sense modulated signals under very low SNR. Simulation results are provided to verify the performance of the algorithm proposed under a low SNR and noise fluctuation. Compared with the maximum and minimum eigenvalue (MME) algorithm, frequency domain autocorrelation-based (FD-AC) algorithm and statistical knowledge autocorrelation-based (SKAB) algorithm, it improves about 4 dB margin in SNR.

show abstract