Word-Fi: Accurate Handwrite System Empowered by Wireless Backscattering and Machine Learning

Ren, Dong; Zhang, Yizhuo; Xiao, Ning; Zhou, Hao; Li, Xiangyang; Qian, Jianwei; Yang, Panlong

doi:10.1109/mnet.2018.1700441

Cited by 13 publications

(2 citation statements)

References 13 publications

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“…Word-Fi is an accurate handwriting system using BackCom and machine learning to recognize tiny gestures [237]. In order to maximize the network performance in an RF-powered cognitive radio network, an online reinforcement learning approach was proposed by Van et al [238].…”

Section: Artificial Intelligence and Backcommentioning

confidence: 99%

Backscatter Communications: Inception of the Battery-Free Era—A Comprehensive Survey

Memon

Saxena

Roy³

et al. 2019

Electronics

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The ever increasing proliferation of wireless objects and consistent connectivity demands are creating significant challenges for battery-constrained wireless devices. The vision of massive IoT, involving billions of smart objects to be connected to the cellular network, needs to address the problem of uninterrupted power consumption while taking advantage of emerging high-frequency 5G communications. The problem of limited battery power motivates us to utilize radio frequency (RF) signals as the energy source for battery-free communications in next-generation wireless networks. Backscatter communication (BackCom) makes it possible to harvest energy from incident RF signals and reflect back parts of the same signals while modulating the data. Ambient BackCom (Amb-BackCom) is a type of BackCom that can harvest energy from nearby WiFi, TV, and cellular RF signals to modulate information. The objective of this article is to review BackCom as a solution to the limited battery life problem and enable future battery-free communications for combating the energy issues for devices in emerging wireless networks. We first highlight the energy constraint in existing wireless communications. We then investigate BackCom as a practical solution to the limited battery life problem. Subsequently, in order to take the advantages of omnipresent radio waves, we elaborate BackCom tag architecture and various types of BackCom. To understand encoding and data extraction, we demonstrate signal processing aspects that cover channel coding, interference, decoding, and signal detection schemes. Moreover, we also describe BackCom communication modes, modulation schemes, and multiple access techniques to accommodate maximum users with high throughput. Similarly, to mitigate the increased network energy, adequate data and power transfer schemes for BackCom are elaborated, in addition to reliability, security, and range extension. Finally, we highlight BackCom applications with existing research challenges and future directions for next-generation 5G wireless networks.

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Section: Artificial Intelligence and Backcommentioning

confidence: 99%

Backscatter Communications: Inception of the Battery-Free Era—A Comprehensive Survey

Memon

Saxena

Roy³

et al. 2019

Electronics

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show abstract

“…Applications of Wi-Fi backscatter other than data communication were considered in some research work. A handwriting input system called Word-Fi was introduced in Reference [29], which mitigates surrounding noise and extracts the weak signals incurred by tiny writing gestures. WiTag [30] finds the location of a Wi-Fi backscatter tag by extracting information such as amplitude, phase, and frequency changes that are associated with the tag’s location from the received signals.…”

Section: Related Workmentioning

confidence: 99%

Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors

Hwang

Lim

Yun

et al. 2019

Sensors

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Ambient backscatter communication enables passive sensors to convey sensing data on ambient RF signals in the air at ultralow power consumption. To extract data bits from such signals, threshold-based decoding has generally been considered, but suffers against Wi-Fi signals due to severe fluctuation of OFDM signals. In this paper, we propose a pattern-matching-based decoding algorithm for Wi-Fi backscatter communications. The key idea is the identification of unique patterns of signal samples that arise from the inevitable smoothing of Wi-Fi signals to filter out noisy fluctuation. We provide the mathematical basis of obtaining the pattern of smoothed signal samples as the slope of a line expressed in a closed-form equation. Then, the new decoding algorithm was designed to identify the pattern of received signal samples as a slope rather than classifying their amplitude levels. Thus, it is more robust against signal fluctuation and does not need tricky threshold configuration. Moreover, for even higher reliability, the pattern was identified for a pair of adjacent bits, and the algorithm decodes a bit pair at a time rather than a single bit. We demonstrate via testbed experiments that the proposed algorithm significantly outperforms conventional threshold-based decoding variants in terms of bit error rate for various distances and data rates.

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Pipeline risk big data intelligent decision-making system based on machine learning and situation awareness

Zhong

Zhang

2022

Neural Comput & Applic

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Word-Fi: Accurate Handwrite System Empowered by Wireless Backscattering and Machine Learning

Cited by 13 publications

References 13 publications

Backscatter Communications: Inception of the Battery-Free Era—A Comprehensive Survey

Backscatter Communications: Inception of the Battery-Free Era—A Comprehensive Survey

Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors

Pipeline risk big data intelligent decision-making system based on machine learning and situation awareness

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