.png){kind=icon}
.png){kind=icon}
Return
Data-Oriented View for Convolutional Coding With Adaptive Irregular Constellations
.png){kind=icon}
OA
AI
DOI：10.1109/LCOMM.2021.3058731.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Current wireless systems offer various use-cases where conventional channel capacity focused performance criteria might not apply. Following the similar perspective, convolutional encoding can find more room due to its low complexity and low decoding delay. Besides, it has been also shown that error performance of a convolutional encoder can be improved further by using adaptive irregular constellations. A new performance measure, data-oriented approach, was recently proposed for the transmission of small data packets, i.e. mission-critical IoT applications, over fading channels. In this letter, delay performance gain resulting from convolution coding optimized irregular constellations is investigated. Then, we derive a new performance criterion based on delay and finite block length constraints. Based on this criterion, we design irregular constellations together with convolutional coding for short packet transmission.
Keywords:
Delays
Convolutional codes
Convolution
Decoding
Signal to noise ratio
Iterative decoding
Fading channels
Small data transmission
delay outage rate
constellation design
error correction coding
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
View more organizations