Energy-Efficient Raptor-like LDPC Coding Scheme Design and Implementation for IoT Communication Systems

Abstract

The large number of inexpensive and energy-efficient terminals in IoT systems is one of the emerging elements of the recent landscape of information and communication technologies. IoT nodes are usually embedded systems with limited processing power devices and strict requirements on energy consumption. In this paper, we consider the design and implementation of a part of the IoT communication uplink stack, namely the error correction coding scheme, for energy-efficient operation. We examine how an efficient rate-adaptive coding scheme, namely the Raptor-like (RL) quasi-cyclic (QC) subclass of low-density parity check (LDPC) codes, can be applied. We present an encoding algorithm designed for an embedded CPU; a respective QC-RL-LDPC decoding scheme, based on typical LDPC iterative decoding; and a combined design procedure for construction of the QC-RL-LDPC parity check matrices. Next, we conduct experiments to explore the time intervals for implemented encoding and the goodput of the coding system with incremental redundancy. We provide the statistical results by combining the measured energy consumption of the encoder and the simulated radio transmitter energy consumption. We demonstrate the comparison of normalized energy consumption statistics with the fixed-rate LDPC coding case. As conclusion, we note that under an unknown channel corruption level, the short block QC-RL-LDPC code implemented in the CPU can achieve higher energy efficiency per information bit compared to a fixed-rate LDPC code. Future work will include an extension of the work to nonbinary QC-RL-LDPC coding design.

A Novel Energy-Efficient Coding Based on Coordinated Group Signal Transformation for Image Compression in Energy-Starved Systems

“…Optimizing MIMO technology, switching off subcarriers in orthogonal frequency division multiplexing [17,18], and applying multidimensional constellation diagrams [19,20] are examples of the channel-level methods. Finally, the source encoding level is related to various energy-efficient coding [21][22][23][24], energy-efficient redundancy coding [25] techniques, and image compression (both lossy and lossless) [26,27]. The rapid progress in multimedia and visual Internet of Things (IoT) technologies [28,29], coupled with the growing reliance on microsatellites and unmanned aerial vehicles (UAVs) for remote sensing of the Earth [30][31][32], highlight the need for improved image compression technologies.…”