arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.20198cs.ITcs.SYeess.SYmath.IT
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最小化联合信源信道编码系统的端到端时延

Minimizing End-to-End Latency for Joint Source-Channel Coding Systems

Kaiyi Chi, Qianqian Yang, Yuanchao Shu, Zhaohui Yang, Zhiguo Shi

AI总结:

本文针对上行联合信源信道编码系统,通过建立端到端时延模型并求解NP难优化问题,推导出最优压缩比、截断阈值和资源分配的闭式解,并提出低复杂度启发式算法,显著降低端到端时延。

摘要:

尽管现有研究已突显基于深度学习(DL)的联合信源信道编码(JSCC)方案在提升传输效率方面的优势,但它们往往忽略了部署阶段资源管理这一关键方面。本文提出一种在上行JSCC系统中最小化传输时延的方法。我们首先分析端到端时延与任务性能之间的相关性,并据此建立每个设备的端到端时延模型。然后,我们构建一个以最小化所有设备中最大端到端时延为目标的非凸优化问题,并证明该问题是NP难的。接着,我们将原始问题转化为一个更易处理的问题,并由此推导出最优压缩比、截断阈值选择策略和资源分配策略的闭式解。我们进一步利用最优解结构的洞察,提出一种低复杂度的启发式算法。仿真结果表明,所提出的最优算法和启发式算法均显著降低了端到端时延。值得注意的是,所提出的启发式算法在性能上几乎与最优解相同,但计算复杂度却大大降低。

英文摘要:

While existing studies have highlighted the advantages of deep learning (DL)-based joint source-channel coding (JSCC) schemes in enhancing transmission efficiency, they often overlook the crucial aspect of resource management during the deployment phase. In this paper, we propose an approach to minimize the transmission latency in an uplink JSCC-based system. We first analyze the correlation between end-to-end latency and task performance, based on which the end-to-end delay model for each device is established. Then, we formulate a non-convex optimization problem aiming at minimizing the maximum end-to-end latency across all devices, which is proved to be NP-hard. We then transform the original problem into a more tractable one, from which we derive the closed form solution on the optimal compression ratio, truncation threshold selection policy, and resource allocation strategy. We further introduce a heuristic algorithm with low complexity, leveraging insights from the structure of the optimal solution. Simulation results demonstrate that both the proposed optimal algorithm and the heuristic algorithm significantly reduce end-to-end latency. Notably, the proposed heuristic algorithm achieves nearly the same performance to the optimal solution but with considerably lower computational complexity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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