An Estimation-Range Extended Autocorrelation-Based Frequency Estimator

Wang, Gang; Chen, Fangjiong

doi:10.1155/2009/961938

Cited by 11 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The periodogram-based estimators use the Discrete Fourier Transform (DFT) for a coarse search and an interpolation technique for a fine search [13]. In correlation-based singletone frequency estimation, consider the single-tone model as per Equation (1).…”

Section: Frequency Estimation Based On Autocorrelationmentioning

confidence: 99%

Frequency Estimation of Single-Tone Sinusoids Under Additive and Phase Noise

Almoosawy¹,

Hussain²,

Murad³

2014

ijacsa

View full text Add to dashboard Cite

Section: Frequency Estimation Based On Autocorrelationmentioning

confidence: 99%

Frequency Estimation of Single-Tone Sinusoids Under Additive and Phase Noise

Almoosawy¹,

Hussain²,

Murad³

2014

ijacsa

View full text Add to dashboard Cite

“…Correlation methods can be very attractive for hardware implementation, IoT, sensors, and software-defined ratio (SDR), as they are much more computationally efficient than spectral techniques are. However, those techniques are far less accurate compared to Fourier-based techniques, especially under low values of signal-to-noise ratio (SNR) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Almayyali

Hussain

2021

Sensors

View full text Add to dashboard Cite

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. A DL-based approach can properly work using a minimal number of input nodes N at which classical methods fail. DL could use as few as two layers while having two or three nodes for each, with the complexity of O{N} compared with discrete Fourier transform (DFT)-based FE with O{Nlog2 (N)} complexity. Furthermore, less N is required for DL. Therefore, DL can significantly reduce FE complexity, memory cost, and power consumption, which is attractive for resource-limited systems such as some Internet of Things (IoT) sensor applications. Reduced complexity also opens the door for hardware-efficient implementation using short-word-length (SWL) or time-efficient software-defined radio (SDR) communications.

show abstract

“…Several classical methods have been proposed to estimate the frequency of a single-tone sinusoid, mostly based on Fourier analysis [3]. Correlation methods can be very attractive for hardware implementation as well as for IoT, sensors, and software-defined ratio (SDR) as they are much more computationally efficient than spectral techniques; however, those techniques are far less accurate than Fourier-based techniques, especially under low values of signal-to-noise ratio (SNR) [4]. Phase-locked loops (PLLs) are widely used in communication systems to handle this problem; however, PLLs can be slower that spectral or correlative techniques as they need time for locking [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning versus Spectral Techniques for Frequency Estimation of Single-Tones: Reduced Complexity for SDR and IoT Sensor Communications

Almayyali¹,

Zm²

2021

Preprint

View full text Add to dashboard Cite

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

show abstract

An Estimation-Range Extended Autocorrelation-Based Frequency Estimator

Cited by 11 publications

References 16 publications

Frequency Estimation of Single-Tone Sinusoids Under Additive and Phase Noise

Frequency Estimation of Single-Tone Sinusoids Under Additive and Phase Noise

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Deep Learning versus Spectral Techniques for Frequency Estimation of Single-Tones: Reduced Complexity for SDR and IoT Sensor Communications

Contact Info

Product

Resources

About