知识增强的语义通信接收端设计

doi:10.11959/j.issn.1000-436x.2023106

Abstract

Abstract:

To address the problem that existing semantic communication do not make sufficient use of prior knowledge and have limited decoding capability at the receiver side, a knowledge enhanced semantic communication framework was proposed, in which the receiver could more actively utilize the prior knowledge in the knowledge base for semantic reasoning and decoding, without extra modifications to the neural network structure of the transmitter.Specifically, a transformer-based knowledge extractor was designed to find relevant factual triples for the received noisy signal.Extensive simulation results on the WebNLG dataset demonstrate that the proposed framework has significantly improved performance on the basis of knowledge graph enhanced decoding.

Key words: semantic communication, knowledge graph, deep learning, knowledge extraction, semantic decoding

CLC Number:

TN92

Rongpeng LI, Bingyan WANG, Honggang ZHANG, Zhifeng ZHAO. Design of knowledge enhanced semantic communication receiver[J]. Journal on Communications, 2023, 44(6): 70-76.

Figures/Tables 9

参数	含义
$S_{β} (\cdot)$ 和 $S_{γ}^{- 1} (\cdot)$	语义编码器和解码器
$C_{α} (\cdot)$ 和 $C_{δ}^{- 1} (\cdot)$	信道编码器和解码器
$K_{θ} (\cdot)$	知识提取器
$s$ 和 $\hat{s}$	发送和接收到的句子
$h$	语义编码向量
$\hat{h}$	接收端信道解码向量
$x$	发送的信号
$y$	接收的信号
$k$	知识向量
$t$	知识库索引向量
n_t	知识库中三元组的数量
N	句子长度
w	知识提取过程的权重参数
$f_{k} (\cdot)$	知识嵌入过程

References 15

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参数	值
训练集数量/个	24 467
测试集数量/个	2 734
权重参数w	0.02
优化器	Adam
Batch大小	32
模型维度	128
学习率	10^-4

信噪比/dB	单一模型	特定信噪比模型
-4	0.651 4	0.671 8
-2	0.793 6	0.812 6
0	0.866 1	0.866 1
2	0.902 5	0.913 4
4	0.916 4	0.920 1

Design of knowledge enhanced semantic communication receiver

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