5G-A车联网确定性网络中的语义通信系统

doi:10.3785/j.issn.1008-973X.2025.05.010

浙江大学学报(工学版)

2025, Vol. 59

Issue (5): 964-972 DOI: 10.3785/j.issn.1008-973X.2025.05.010

计算机技术、信息工程

5G-A车联网确定性网络中的语义通信系统

栾宏之1(

),李书才1,李屹2,钱传杰2,*(

),梅琼2

1. 山东电力工程咨询院有限公司综合智慧能源部，山东济南 250011
2. 江苏未来网络集团有限公司战略项目部，江苏南京 211111

Semantic communication system in deterministic networking for5G-advanced vehicular network

Hongzhi LUAN1(

),Shucai LI1,Yi LI2,Chuanjie QIAN2,*(

),Qiong MEI2

1. Comprehensive Smart Energy Department., Shandong Electric Power Engineering Consulting Institute Limited Company, Jinan 250011, China
2. Strategic Project Department, Jiangsu Future Network Group, Nanjing 211111, China

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摘要：

针对现有语义通信（SC）系统编码时间较长，无法直接应用于5G-Advanced技术框架下，需要处理大量高实时性、低延迟数据的智能车联网系统（V2X）中确定性网络（DN）的问题，提出针对5G-Advanced智能车联网中确定性网络设计的截止时间敏感的语义通信系统（DDA-SC）. 该系统采用截止时间敏感的语义编码器，通过控制编码深度适应数据截止时间，确保在有效时间内完成编码与传输. 为了提高各编码截止时间下的传输可靠性，提出基于编码深度的信噪比感知网络. 该网络在训练时将SNR映射为编码深度相关的相关信噪比（R-SNR），利用注意力机制进行R-SNR感知，以在不同编码截止时间下更好地抵御信道噪声. 实验结果表明，当编码截止时间< 5 μs时，该系统在CARS-196汽车识别任务上的准确度较不考虑截止时间的系统提高80.31%以上. 结果表明，DDA-SC可以在极低编码截止时间下完成一般语义通信系统无法完成的任务，验证了该方法的有效性.

关键词： 车联网(V2X); 语义通信; 5G-Advanced; 确定性网络; 联合信源信道编码

Abstract:

A deadline-aware semantic communication (DDA-SC) system specifically tailored for deterministic network in 5G-Advanced intelligent vehicle-to-everything (V2X) environments was proposed in order to overcome the challenge of prolonged encoding time in current semantic communication (SC) systems, which impedes their direct deployment in deterministic networking (DN) for 5G-Advanced vehicle-to-everything (V2X) networks—particularly critical for intelligent transportation systems requiring high-throughput and low-latency data processing. A deadline-aware semantic encoder (DDA-SE) was employed, which adjusted the depth of semantic encoding to meet data deadlines, ensuring timely encoding and transmission. A signal-to-noise ratio (SNR) aware network based on encoding depth was introduced in order to enhance transmission reliability across different encoding deadlines. SNR was mapped to relevant signal-to-noise ratio (R-SNR) based on encoding depth during training, using an attention mechanism to sense R-SNR and better resist channel noise under various encoding deadlines. The experimental results show that this system achieve an accuracy improvement of over 80.31% in the CARS-196 vehicle identification task compared with systems that do not consider data deadlines when encoding deadlines are shorter than 5 microseconds. Results demonstrate that DDA-SC can accomplish tasks under extremely low encoding deadlines that are unachievable by conventional semantic communication systems, thus verifying the effectiveness of the method.

Key words: vehicle-to-everything (V2X) semantic communication 5G-Advanced deterministic networking joint source and channel coding

收稿日期: 2024-07-12 出版日期: 2025-04-25

CLC:

TN 914

基金资助: 北京市自然科学基金资助项目 (L222043).

通讯作者: 钱传杰 E-mail: Luanhongzhi@sdepci.com;qianchuanjie@fngroup.cn

作者简介: 栾宏之（1970—），男，高级工程师，从事5G-Advanced和车联网的研究. orcid.org/0009-0002-4168-4640. E-mail：Luanhongzhi@sdepci.com

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引用本文:

栾宏之,李书才,李屹,钱传杰,梅琼. 5G-A车联网确定性网络中的语义通信系统[J]. 浙江大学学报(工学版), 2025, 59(5): 964-972.

Hongzhi LUAN,Shucai LI,Yi LI,Chuanjie QIAN,Qiong MEI. Semantic communication system in deterministic networking for5G-advanced vehicular network. Journal of ZheJiang University (Engineering Science), 2025, 59(5): 964-972.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.05.010 或 https://www.zjujournals.com/eng/CN/Y2025/V59/I5/964

图 1 联合信源信道编码系统

图 2 截止时间敏感的语义通信系统

图 3 基于编码深度的信噪比感知网络

图 4 截止时间敏感的语义通信系统的训练过程

算法2　截止时间敏感的语义通信系统的训练算法
输入：训练集

${{D}_{\text{I}}} = \{ ({{\boldsymbol{x}}^{(k)}},{\boldsymbol{y}}_{{\text{truth}}}^{(k)}),k = 1,2, \cdots ,N\} $

.
需要：初始化的补偿因子

$\gamma = \{ {\gamma _1},{\gamma _2}, \cdots ,{\gamma _n}\} $

、信道模拟函数

${\mathrm{CH}}({{\boldsymbol{T}}_{\boldsymbol{x}}},s)$

、编码器的参数集

$({\boldsymbol{\theta}} ,{\boldsymbol{\varTheta}} ) = \{ ({{\boldsymbol{\theta}} _1}, {{\boldsymbol{\varTheta}} _1}), ({{\boldsymbol{\theta}} _2},{{\boldsymbol{\varTheta}} _2}), \cdots ,({{\boldsymbol{\theta}} _n},{{\boldsymbol{\varTheta}} _n})\} $

、解码器参数

${\boldsymbol{\phi}} $

、训练轮次T、编码器参数的学习率

$\alpha $

、解码器参数的学习率

$\beta $

、当前迭代轮次

$j$

、总误差

$L$

.
输出：

${\boldsymbol{\theta}} $

、

${\boldsymbol{\phi}} $

.
1 初始化：

$j \leftarrow 0$

,初始化

${\boldsymbol{\theta}} $

、

${\boldsymbol{\phi}} $

;
2 对训练集

${{D}_{\text{I}}}$

中的样本随机排序;
3 While (

$j < $

T) Do
4 从0 dB到20 dB中随机采样一个SNR，记为

$s$

5 For

$k = 1,2, \cdots, N$

Do
6　　

$L \leftarrow 0$

${\boldsymbol{z}}_0^{(k)} \leftarrow {{\boldsymbol{x}}^{(k)}}$

;
7　　For

$i = 1 ,\cdots ,n$

Do
8　　　　

$ {\boldsymbol{z}}_i^{(k)} \leftarrow {f_{{{\boldsymbol{\varTheta}} _i}}}({f_{{{\boldsymbol{\theta}} _i}}}({\boldsymbol{z}}_{i - 1}^{(k)}),{\gamma _i} s)$

;
9　　　　

$\hat {\boldsymbol{z}}_i^{(k)} \leftarrow {\mathrm{CH}}({\boldsymbol{z}}_i^{(k)},{\gamma _i} s)$

;
10　　　

$ {\boldsymbol{y}}_i^{(k)} \leftarrow {f_{\boldsymbol{\phi}} }(\hat {\boldsymbol{z}}_i^{(k)}) $

;
11　　　

$L \leftarrow L+L\;({\boldsymbol{y}}_i^{(k)},{\boldsymbol{y}}_{{\text{turth}}}^{(k)})$

;
12　

${\boldsymbol{\theta}} \leftarrow {\boldsymbol{\theta}} - \alpha {\nabla _{\boldsymbol{\theta}} }L$

;

${\boldsymbol{\varTheta}} \leftarrow {\boldsymbol{\varTheta}} - \alpha {\nabla _{\boldsymbol{\varTheta}} }L $

;

$\gamma \leftarrow \gamma - \alpha {\nabla _\gamma }L$

;
　

${\boldsymbol{\phi}} \leftarrow {\boldsymbol{\phi}} - \beta {\nabla _{\boldsymbol{\phi}} }L$

;

$j \leftarrow j+1$

;
13 Return

${\boldsymbol{\theta}} $

${\boldsymbol{\varTheta}} $

$\gamma $

${\boldsymbol{\phi}} $

表 1 截止时间敏感的语义通信系统的模型参数

表 2 训练超参数的设置

图 5 编码深度对模型性能的影响

图 6 不同编码截止时间下的模型性能

图 7 不同信噪比下的模型性能

图 8 基于编码深度的信噪比感知网络的消融实验

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