Document Type
Journal Article
Publication Title
Sensors
Volume
21
Issue
8
Publisher
MDPI
School
School of Engineering
RAS ID
36666
Funders
Edith Cowan University
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
This work is licensed under a Creative Commons Attribution 4.0 License.
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