Document Type

Journal Article

Publication Title

Sensors

Volume

21

Issue

8

Publisher

MDPI

School

School of Engineering

RAS ID

36666

Funders

Edith Cowan University

Comments

Almayyali, H. R., & Hussain, Z. M. (2021). Deep learning versus spectral techniques for frequency estimation of single tones: Reduced complexity for software-defined radio and iot sensor communications. Sensors, 21(8), article 2729. https://doi.org/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 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.

DOI

10.3390/s21082729

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Research Themes

Securing Digital Futures

Priority Areas

Artificial intelligence and autonomous systems

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