Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 25 February 2021 / Approved: 26 February 2021 / Online: 26 February 2021 (15:22:14 CET)

A peer-reviewed article of this Preprint also exists.

Almayyali, H.R.; Hussain, Z.M. Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications. Sensors 2021, 21, 2729. Almayyali, H.R.; Hussain, Z.M. Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications. Sensors 2021, 21, 2729.

Journal reference: Sensors 2021, 21, 2729
DOI: 10.3390/s21082729

Abstract

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.

Keywords

Frequency Estimation; Deep Learning (DL); Sensors; Internet of Things (IoT); Short Word-Length (SWL); Software-Defined Radio (SDR); Parallel-Computing FFT; Low-Power; Low-Cost; Biomedical Sensors.

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.