Towards Green Communication: Soft Decoding Scheme for OOK Signals in Zero-Energy Devices

TL;DR

This paper introduces a soft-decision decoding scheme for OOK signals in zero-energy IoT devices, significantly improving communication range and reliability while maintaining energy efficiency.

Contribution

A novel soft-decision decoder for OOK signals is proposed, with closed-form and approximate LLR expressions, enhancing detection performance in low-power IoT communications.

Findings

Significant coverage gain with soft decoding for convolutional codes.

Enhanced detection performance in noisy environments.

Improved reliability for zero-energy IoT devices.

Abstract

The booming of Internet-of-Things (IoT) is expected to provide more intelligent and reliable communication services for higher network coverage, massive connectivity, and low-cost solutions for 6G services. However, frequent charging and battery replacement of these massive IoT devices brings a series of challenges. Zero energy devices, which rely on energy-harvesting technologies and can operate without battery replacement or charging, play a pivotal role in facilitating the massive use of IoT devices. In order to enable reliable communications of such low-power devices, Manchester-coded on-off keying (OOK) modulation and non-coherent detections are attractive techniques due to their energy efficiency, robustness in noisy environments, and simplicity in receiver design. Moreover, to extend their communication range, employing channel coding along with enhanced detection schemes is crucial. In this paper, a novel soft-decision decoder is designed for OOK-based low-power receivers to enhance their detection performance. In addition, exact closed-form expressions and two simplified approximations are derived for the log-likelihood ratio (LLR), an essential metric for soft decoding. Numerical results demonstrate the significant coverage gain achieved through soft decoding for convolutional code.