.png){kind=icon}
返回
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}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
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总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.5W
机构
引用论文
Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks.png)
IEEE NETWORKOAAI
IEEE NETWORKIF6.3
OAAI分享收藏分享收藏Ultra-Reliable Low-Latency Transmission of Small Data Over Fading Channels: A Data-Oriented Analysis
PREAI
分享收藏分享收藏
查看更多内容