Blind Signal Detection Using a Linear Antenna Array: An Experimental Approach

Abstract-This paper presents a sequential likelihood ratio test (SLRT) detector for spectrum sensing scenarios in cognitive radios (CRs). Similar to other CR detectors, we exploit the structure of the sample covariance matrix of the received signal to achieve detection with minimal information regarding the signal. Unlike the majority of covariance-based CR detectors, the SLRT is a sequential detector that allows for smaller detection delays, which is a prized asset in CR systems. Using methods borrowed from the theory of continuous-time diffusion processes, we derive the statistical properties of the SLRT detector and compare it with an eigenvalue-based sequential detector which has been presented in previous work for CR systems. The comparison also includes detection scenarios with nonGaussian noise to illustrate the robustness of the proposed detector in these situations.

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“…Note that due to the existing correlation between successive L k , equation (39) is only an approximation 5 .…”

Section: Mean Detection Delaymentioning

confidence: 99%

“…(see e.g. [4], [5], [6], [7], [8], [9]). Emphasis is usually put on the reliability of the detector and several strategies have been proposed to improve it, using for instance collaborative sensing schemes [10], [3] and nonparametric detectors [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Sequential Likelihood Ratio Test for Cognitive Radios

Renard

Lampe

Horlin

2016

IEEE Trans. Signal Process.

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“…The authors presented the comprehensive analysis of strength-based optimum signal detection model for concentration-encoded molecular communication with spike transmission based on amplitude-shift keying and on-off keying modulations [7]. The effect of varying the antenna spacing on the received signal correlation was investigated and its subsequent effect on the detection performance was shown by Bhatti et al [8]. Under a Neyman-Pearson hypothesis-testing problem model, Lei et al [9] proposed a new detection scheme referred to as the likelihood ratio test with sparse estimation.…”

Section: Introductionmentioning

confidence: 99%

“…After the deep analysis of receiver operating characteristic analysis [6], received signal correlation [8], and bandwidth minimization [12], we study the sensor deployment density of mobile crowd network and proposed the signal hole repair strategy.…”

Section: Introductionmentioning

confidence: 99%

Signal hole repair strategy based on sensor deployment density for mobile crowd network

2017

J Embedded Systems

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In order to reduce the signal holes in wireless sensor networks, we proposed a sensor deployment density-aware signal hole repair strategy of mobile crowd network. On the one hand, based on the multi-dimensional connected graph, the density continuous jitter problem of the sensor network is solved effectively. Sensor density prediction method improves the efficiency of random deployment of sensor nodes. Based on the above schemes, the sensor deployment density sensing model is proposed. On the other hand, according to the regional geometry, diversity of the sensor network channel and density can detect the time domain channel impulse response signal successfully. Finally, the network signal is detected by the signal detection of the mobile crowd network. The signal holes can be detected and repaired based on coverage density and crowd. Experimental results show that the proposed algorithm has outstanding performance in terms of signal strength and signal void ratio compared with the energy-aware repair algorithm.

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“…Under the given dimension restriction, different antenna spacing was examined to optimize the DOA estimation. The authors in [15] suggested antenna spacing of 0.1 for a proposed software defined radio system. Therefore, impedance matching was suggested to avoid the degradation with such close spacing [15].…”

Section: Introductionmentioning

confidence: 99%

DOA estimation in MIMO systems with Compressive Sensing for future handsets

Alawsh

Muqaibel

Sharawi

2015

2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)

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Future handsets are characterized by their small size which limits the number of antennas and the spacing between them. This paper mainly focuses on Direction-ofArrival (DOA) estimation in Multiple-Input-Multiple-Output (MIMO) systems based on Compressive Sensing (CS) with practical limitations. The main objective is to strike an optimal balance between complexity and accuracy. A comparison between important DOA estimation algorithms is presented including: Beamforming, Capon, MUSIC, and -Singular Value Decomposition ( -SVD). When considering two antennas with practical antenna spacing limitations, simulation shows that the Root Mean Squared Error (RMSE) of the estimated DOAs is approximately the same for all algorithms in the presence of a single source. The smallest RMSEs are achieved when the spacing between the antennas relative to the wavelength = 1/3. The -SVD algorithm is recommended due to the narrow beamwidth and high resolution.

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Blind Signal Detection Using a Linear Antenna Array: An Experimental Approach

Cited by 7 publications

References 25 publications

Sequential Likelihood Ratio Test for Cognitive Radios

Sequential Likelihood Ratio Test for Cognitive Radios

Signal hole repair strategy based on sensor deployment density for mobile crowd network

DOA estimation in MIMO systems with Compressive Sensing for future handsets

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