Title
Design Of Segmented Crc-Aided Spinal Codes For Iot Applications
Abstract
Rateless spinal codes can achieve reliable transmission with high throughput performance, which is required by some power-constrained applications, such as internet of things (IoT). In this study, the cyclic redundancy check (CRC) is divided into segments. We design the segmented CRC-aided (SCA) spinal codes and propose a novel hybrid decoding algorithm. The decoder can terminate the decoding process earlier when an error decoding is detected in any segment. Moreover, a more targeted symbol transmission strategy after decoding errors occur is provided and we call it as the transmitting redundant symbols for specific segments (RSSS) strategy. The RSSS strategy saves the transmitting symbols by transmitting a variable number of symbols, thus improving the throughput of the system. Furthermore, we design a new tail-biting structure for SCA-spinal codes to compensate for the disadvantage of poor error detection ability for short segment CRC bits. The simulation results show that the proposed SCA-spinal codes can reduce the decoding complexity and improve the throughput of the system. The transmission delay can also be reduced by dividing the information bits and CRC bits into an appropriate number of segments.
Year
DOI
Venue
2020
10.1049/iet-com.2019.0907
IET COMMUNICATIONS
Keywords
DocType
Volume
error statistics, channel coding, cyclic redundancy check codes, sequential decoding, Internet of Things, computer network reliability, poor error detection ability, short segment CRC bits, SCA-spinal codes, decoding complexity, transmission delay, segmented CRC-aided spinal codes, IoT applications, rateless spinal codes, high throughput performance, power-constrained applications, cyclic redundancy check, novel hybrid decoding algorithm, decoder, decoding process, error decoding, targeted symbol transmission strategy, transmitting redundant symbols, specific segments strategy, RSSS strategy, transmitting symbols, high signal-to-noise ratio regions
Journal
14
Issue
ISSN
Citations 
20
1751-8628
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Hongxiu Bian100.34
Rongke Liu212735.79
Kaushik Aryan3122.84
Yingmeng Hu400.34
J. Thompson53922267.43