Performance of Frequency Estimation Techniques with Phase Noise in mm-Wave Based 5G Systems

Qiu, Jiaqi; Kandeepan, Sithamparanathan

doi:10.1109/ams.2017.34

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Moreover, the estimation accuracy shown in Figure 5 cannot be achieved in real‐life. One source of this problem is the impact of hardware non‐idealities [42], for example, Phase Noise, on the accuracy of subspace‐based estimation techniques [43, 44].…”

Section: Resultsmentioning

confidence: 99%

A multi‐antenna super‐resolution passive Wi‐Fi radar algorithm: Combined model order selection and parameter estimation

Yildirim

Storrer

Doncker

et al. 2022

IET Radar Sonar & Navi

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In recent years, Wi‐Fi has become the main gateway that connects users to the Internet. Considering the availability of Wi‐Fi signals, and their suitability for channel estimation, IEEE established the Wi‐Fi Sensing (WS) Task Group whose purpose is to study the feasibility of Wi‐Fi‐based environment sensing. However, Wi‐Fi signals are transmitted over limited bandwidths with a relatively small number of antennas in bursts, fundamentally limiting the range, Angle‐of‐Arrival and speed resolutions. This paper presents a super‐resolution algorithm to perform the parameter estimation in a quasi‐monostatic WS scenario. The proposed algorithm, RIVES, estimates the range, Angle‐of‐Arrival and speed parameters with Vandermonde decomposition of Hankel matrices. To estimate the size of the signal subspace, RIVES uses a novel Model Order Selection method which eliminates spurious noise targets based on their distance to the noise and signal subspaces. Various scenarios with multiple targets are simulated to show the robustness of RIVES. In order to prove its accuracy, real‐life indoor experiments are conducted with human targets by using Software Defined Radios.

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Section: Resultsmentioning

confidence: 99%

A multi‐antenna super‐resolution passive Wi‐Fi radar algorithm: Combined model order selection and parameter estimation

Yildirim

Storrer

Doncker

et al. 2022

IET Radar Sonar & Navi

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show abstract

“…Section IV above implies that the inverse of Σ r can be computed recursively as N increases, and this recursive inversion will allow (33) and ( 34) to be computed recursively in explicit forms. That is, we have (1 ) , and (N T Σ r −1 N) (N ) = E (N ) given exactly the same as those in (26), only with the changes: (1) . (35) Next, we consider to derive the unbiased CRLBs for (ω, φ).…”

Section: A Crlbs and Performance Of ML Estimators For (ω φ)mentioning

confidence: 98%

“…The threshold SNR of the LMMSE-WPA estimation is about 7 dB, and its estimation accuracy is not sensitive to the small changes of the frequency ω 0 as expected. Moreover, several classical frequency estimation techniques are mostly considered for designing practical systems [1]. The frequency-domain method is based on the interpolation of Fourier coefficients.…”

Section: E Comparison With the Lmmse-wpa And Improved Kay's Estimatorsmentioning

confidence: 99%

“…As Fig. 10 shows, the improved Kay's estimator in [1,] only performs slightly worse than the LMMSE implementation.…”

Section: E Comparison With the Lmmse-wpa And Improved Kay's Estimatorsmentioning

confidence: 99%

“…important issue in communications [1]- [3], biomedical engineering [4]- [6], radar/sonar applications [7], [8], and other areas of signal processing, e.g., power-quality monitoring in the power grid [9], [10]. All these physical applications can resort to almost the same signal model for theoretical performance analysis, where a single sinusoidal carrier wave is used.…”

mentioning

confidence: 99%

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Joint ML/MAP Estimation of the Frequency and Phase of a Single Sinusoid With Wiener Carrier Phase Noise

Wang

Quan

et al. 2022

IEEE Trans. Signal Process.

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We address here the issue of jointly estimating the angle parameters of a single sinusoid with Wiener carrier phase noise and observed in additive, white, Gaussian noise (AWGN). We develop the theoretical foundation for time-domain, phase-based, joint maximum likelihood (ML) estimation of the unknown carrier frequency and the initial carrier phase, with simultaneous maximum a posteriori probability (MAP) estimation of the time-varying carrier phase noise. The derivation is based on the amplitude and phase-form of the noisy received signal model together with the use of the best, linearized, additive observation phase noise model due to AWGN. Our newly derived estimators are closed-form expressions, consisting of both the phase and the magnitude of all the received signal samples. More importantly, they all have a low-complexity, sample-by-sample iterative processing structure, which can be implemented iteratively in real-time. As a basis for comparison, the Cramer-Rao lower bound (CRLB) for the ML estimators and the Bayesian CRLB (BCRLB) for the MAP estimator are derived in the presence of carrier phase noise, and the results simply depend on the signal-to-noise ratio (SNR), the observation length and the phase noise variance. It is theoretically shown that the estimates obtained are unbiased, and the mean-square error (MSE) of the estimators attain the CRLB/BCRLB at high SNR. The MSE performance as a function of the SNR, the observation length and the phase noise variance is verified using Monte Carlo simulation, which shows a remarkable improvement in estimation accuracy in large phase noise.

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A new frequency estimation algorithm for IIoT applications and low-cost instrumentation

Campobello

Segreto

Donato

2020

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Performance of Frequency Estimation Techniques with Phase Noise in mm-Wave Based 5G Systems

Cited by 6 publications

References 23 publications

A multi‐antenna super‐resolution passive Wi‐Fi radar algorithm: Combined model order selection and parameter estimation

A multi‐antenna super‐resolution passive Wi‐Fi radar algorithm: Combined model order selection and parameter estimation

Joint ML/MAP Estimation of the Frequency and Phase of a Single Sinusoid With Wiener Carrier Phase Noise

A new frequency estimation algorithm for IIoT applications and low-cost instrumentation

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