面向万物智联的语义通信网络

doi:10.11959/j.issn.2096-3750.2021.00209

Abstract

Abstract:

With the fast growing demand for the wireless network intelligence, the future communication system will transform from the traditional data-oriented solution to a novel intelligence-of-everything (IoE)-based architecture.Semantic communication is a new communication technology which involves the meaning of message into the communication process.It is believed that semantic communication will have the potential to serve as the fundamental paradigm for the future IoE.The relationship between the semantic communication and IoE was discussed and the basic models and fundamental components of semantic communication were introduced.By discussing the limitations of point-to-point semantic communication, it was argued that the knowledge sharing and resource convergence-based semantic communication networking would be ideal for supporting the future massive scales of IoE systems.The basic components of the semantic communication networking system were discussed and a federated edge intelligence-based semantic communication networking architecture as a case study was considered.Simulation results show that semantic communication networking has the potential to further reduce the resource demand and improve the efficiency of semantic communication.Finally, open problems for future research were discussed.

Key words: semantic communication, intelligence of everything, edge intelligence, federated learning

CLC Number:

TN929.5

Guangming SHI, Yong XIAO, Yingyu LI, Dahua GAO, Xuemei XIE. Semantic communication networking for the intelligence of everything[J]. Chinese Journal on Internet of Things, 2021, 5(2): 26-36.

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References 26

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数据集名称	网络模型	本地存储/KB	有标签数据量/个	目标精度	模型训练耗时/s
MNIST	2层卷积层	46 023	48 000	98.8%	344
MNIST	1层全连接层	46 023	48 000	98.8%	344
Fashion-MNIST	2层卷积层	46 092	48 000	87.0%	369
Fashion-MNIST	1层全连接层	46 092	48 000	87.0%	369
	2层卷积层
CIFAR-10	2层全连接层	121 475	40 000	62.0%	151
	1层线性变换层

数据集	目标精度	网络模型	协同边缘服务器数/个	本地存储/KB	所需标签数据量/个	模型训练耗时/s
CIFAR-10	62.0%	2层卷积层	1	121 475	40 000	151
		2层全连接层	4	30 551	10 000	62
		1层线性变换层	8	15 397	5 000	44
MNIST	98.8%	2层卷积层	1	46 023	48 000	344
		1层全连接层	4	11 570	12 000	126
			8	5 828	6 000	80
FashionMNIST	87.0%	2层卷积层	1	46 092	48 000	369
		1层全连接层	4	11 688	12 000	133
			8	5 887	6 000	121

数据集	传输标签实体数量
	节点数量	10	100	190	550	1 150	1 750	2 350
Cora	压缩率	0.52%	3.84%	9.38%	20.46%	42.61%	64.77%	86.93%
	错误率	50.22%	23.15%	15.69%	10.93%	7.31%	4.36%	1.29%
	节点数量	10	100	2 000	5 000	7 000	10 000	13 000
Amazon Co-Purchase	压缩率	0.11%	0.77%	14.58%	36.40%	50.94%	72.76%	94.57%
	错误率	62.88%	31.40%	27.22%	20.34%	14.40%	7.54%	1.97%
	节点数量	10	20	40	1 000	10 000	13 000	17 000
Coauthor CS	压缩率	0.065%	0.12%	0.23%	5.46%	54.56%	70.92%	92.74%
	错误率	75.06%	33.35%	19.95%	10.91%	6.97%	4.37%	0.98%

Semantic communication networking for the intelligence of everything

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