让无线专用的深度编解码符号适配有速率限制的有线网络。
How to Adapt Wireless DJSCC Symbols to Rate Constrained Wired Networks?
- 通过去除无线冗余,仅编码源相关信息提升传输效率。
- 利用拉格朗日乘子实现连续可调速率编码,降低端到端失真。
- 适合5G/6G混合网络中需动态适应有线速率的场景。
深度联合源信道编码(DJSCC)已成为无线通信中替代传统分离编码的有力方案。现有方法主要聚焦于点对点无线通信,忽视了5G和6G等混合无线-有线网络中的端到端传输效率。针对无线设计的DJSCC符号在长距离有线传输中存在显著冗余,且难以适应有线网络的变速率特性。为此,本文提出一种新型框架——速率可控有线适配器(RCWA),实现对DJSCC符号的冗余感知编码,移除无线通道中的冗余内容,仅将与源相关的有效信息编码为比特。同时,采用拉格朗日乘子法实现连续可调的变率编码,可将给定特征编码至期望速率,从而在满足约束条件下最小化端到端失真。在多个数据集上的大量实验表明,相比现有基线方法,RCWA在率失真性能和鲁棒性上均表现更优。尤其在CIFAR-10数据集上,相较基于神经网络的方法和数字基线,分别获得最高0.7dB和4dB的峰值信噪比增益。
原文摘要 · Abstract (English)
Deep joint source-channel coding (DJSCC) has emerged as a robust alternative to traditional separate coding for communications through wireless channels. Existing DJSCC approaches focus primarily on point-to-point wireless communication scenarios, while neglecting end-to-end communication efficiency in hybrid wireless-wired networks such as 5G and 6G communication systems. Considerable redundancy in DJSCC symbols against wireless channels becomes inefficient for long-distance wired transmission. Furthermore, DJSCC symbols must adapt to the varying transmission rate of the wired network to avoid congestion. In this paper, we propose a novel framework designed for efficient wired transmission of DJSCC symbols within hybrid wireless-wired networks, namely Rate-Controllable Wired Adaptor (RCWA). RCWA achieves redundancy-aware coding for DJSCC symbols to improve transmission efficiency, which removes considerable redundancy present in DJSCC symbols for wireless channels and encodes only source-relevant information into bits. Moreover, we leverage the Lagrangian multiplier method to achieve controllable and continuous variable-rate coding, which can encode given features into expected rates, thereby minimizing end-to-end distortion while satisfying given constraints. Extensive experiments on diverse datasets demonstrate the superior RD performance and robustness of RCWA compared to existing baselines, validating its potential for wired resource utilization in hybrid transmission scenarios. Specifically, our method can obtain peak signal-to-noise ratio gain of up to 0.7dB and 4dB compared to neural network-based methods and digital baselines on CIFAR-10 dataset, respectively.
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