.png){kind=icon}
返回
Trainable Communication Systems: Concepts and Prototype
.png){kind=icon}
OA
AI
DOI：10.1109/TCOMM.2020.3002915.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks (NNs), and demonstrate that training on the bit-wise mutual information (BMI) allows seamless integration with practical bit-metric decoding (BMD) receivers, as well as joint optimization of constellation shaping and labeling. Moreover, we present a fully differentiable neural iterative demapping and decoding (IDD) structure which achieves significant gains on additive white Gaussian noise (AWGN) channels using a standard 802.11n low-density parity-check (LDPC) code. The strength of this approach is that it can be applied to arbitrary channels without any modifications. Going one step further, we show that careful code design can lead to further performance improvements. Lastly, we show the viability of the proposed system through implementation on software-defined radios (SDRs) and training of the end-to-end system on the actual wireless channel. Experimental results reveal that the proposed method enables significant gains compared to conventional techniques.
Keyword:
Receivers
Training
Optical transmitters
Communication systems
Iterative decoding
Optimization
Autoencoder
end-to-end learning
iterative demapping and decoding
code design
geometric shaping
software-defined radio
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:
8.3
论文数:
1.2W
被引数:
3.6W
机构
引用论文
Three-dimensional nanofabrication via ultrafast laser patterning and kinetically regulated material assembly通过超快激光图案化和动力学调节的材料组装进行三维纳米加工.png)
Science
OAAI
ScienceIF0
OAAI分享收藏Model-Based: End-to-End Molecular Communication System Through Deep Reinforcement Learning Auto EncoderIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容