Sample covariance matrix eigenvalues based blind SNR estimation

Hamid, Mohamed; Björsell, Niclas; Slimane, Slimane Ben

doi:10.1109/i2mtc.2014.6860836

Cited by 28 publications

(14 citation statements)

References 16 publications

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“…Another challenge was how to make sure that the SNR we are targeting is the one that we have created. To overcome this challenge, we used a noise measurement method based on the eigenvalues of covariance matrix of the received samples [ 40 , 41 ]. This technique allows one to estimate the noise power and the signal power in a signal at the receiver side.…”

Section: Resultsmentioning

confidence: 99%

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Arjoune

Kaabouch

2018

Sensors

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Sensing the wideband spectrum is an important process for next-generation wireless communication systems. Spectrum sensing primarily aims at detecting unused spectrum holes over wide frequency bands so that secondary users can use them to meet their requirements in terms of quality-of-service. However, this sensing process requires a great deal of time, which is not acceptable for timely communications. In addition, the sensing measurements are often affected by uncertainty. In this paper, we propose an approach based on Bayesian compressive sensing to speed up the process of sensing and to handle uncertainty. This approach takes only a few measurements using a Toeplitz matrix, recovers the wideband signal from a few measurements using Bayesian compressive sensing via fast Laplace prior, and detects either the presence or absence of the primary user using an autocorrelation-based detection method. The proposed approach was implemented using GNU Radio software and Universal Software Radio Peripheral units and was tested on real-world signals. The results show that the proposed approach speeds up the sensing process by minimizing the number of samples while achieving the same performance as Nyquist-based sensing techniques regarding both the probabilities of detection and false alarm.

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Section: Resultsmentioning

confidence: 99%

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Arjoune

Kaabouch

2018

Sensors

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“…The peak of 100 matched filter outputs were recorded and averaged together. The received signal SNR was estimated using an eigenvalue decomposition approach [29]. The received signal can be represented by the sampled matrix…”

Section: A Waveform Propertiesmentioning

confidence: 99%

High-Accuracy Multinode Ranging For Coherent Distributed Antenna Arrays

Ellison

Nanzer

2020

IEEE Trans. Aerosp. Electron. Syst.

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The design and experimental implementation of a waveform for high-accuracy inter-node ranging in a coherent distributed antenna array is presented. Based on a spectrally-sparse high-accuracy ranging waveform, the presented multi-frequency waveform enables high-accuracy ranging between multiple nodes in an array simultaneously, without interference. The waveform is based on a unique time-frequency duplexing approach combining a stepped-frequency waveform with different step cycles per node pair. The waveform also inherently includes beneficial disambiguation properties. The ambiguity function of the waveform is derived, and theoretical bounds on the ranging accuracy are obtained. Measurements were conducted in software-defined radiobased nodes in a three-element distributed array, demonstrating high-accuracy unambiguous ranging between two slave nodes and one master node.

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“…The peak of each matched filter output was upsampled by 1000 points using spline interpolation. At each attenuation level the variance of 100 estimated values was calculated, and the SNR was estimated using an eigenvalue decomposition process [30], [31]. The resulting measured variances, simulations of the performance of the 802.11ac waveform along with the corresponding SFW, and the theoretical bounds can be seen in Fig.…”

Section: Experimental Validationmentioning

confidence: 99%

High-Accuracy Localization Using IEEE 802.11 WiFi Legacy Preamble

Ellison

Nanzer

2020

IEEE Access

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We explore the use of synchronization bits from a standard 802.11 preamble for highaccuracy localization. The multi-tone short training field present in 802.11 standards that use a Legacy preamble is investigated in comparison to a common remote sensing waveform known as the stepped frequency waveform that shares similar waveform characteristics to the preamble. We present the 802.11 standard and the connection to the stepped frequency waveform, derive the theoretical lower bound on the short training field, present a comparison of simulated results, and present experimental wireless measurements demonstrating ranging accuracies on the order of 1.9 mm.

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Sample covariance matrix eigenvalues based blind SNR estimation

Cited by 28 publications

References 16 publications

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

High-Accuracy Multinode Ranging For Coherent Distributed Antenna Arrays

High-Accuracy Localization Using IEEE 802.11 WiFi Legacy Preamble

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