性能

• 新提交
• 替换

查看 最近的 文章

显示 2025年08月06日， 星期三 新的列表

新提交 (展示 1 之 1 条目 )

[1] arXiv:2508.03147 [中文pdf, pdf, 其他]

标题： 一种新型的混合光和STAR IRS系统用于NTN通信 显示英文标题

标题： A Novel Hybrid Optical and STAR IRS System for NTN Communications

Shunyuan Shang, Emna Zedini, Abla Kammoun, Mohamed-Slim Alouini

主题： 性能 (cs.PF) ; 信息论 (cs.IT)

本文提出了一种新颖的非地面网络（NTNs）系统，该系统结合了光学智能反射表面（OIRS）和同时发射和反射智能反射表面（STAR-IRS），以解决下一代通信网络中的关键挑战。 所提出的系统模型具有从光学地面站（OGS）通过安装在高空平台（HAP）上的水平OIRS传输到地球站（ES）的信号。 ES使用具有固定增益的放大并转发（AF）中继进行信号中继，然后通过安装在建筑物上的垂直STAR-IRS传输，以促进与室内和室外用户的通信。 FSO链路结合了（多输入多输出）MIMO技术，本文开发了一个专门设计用于OIRS单元数量超过一个的场景的信道模型。 对于射频（RF）链路，引入了一种新颖且高度精确的近似方法，与基于中心极限定理（CLT）的传统方法相比，提供了更好的准确性。 针对这种新型五跳系统，关键性能指标的闭合形式解析表达式，包括中断概率（OP）、遍历容量和平均比特错误率（BER），以双变量Fox-H函数的形式推导出来。 还给出了高信噪比下的渐近表达式，提供了对系统分集阶数的见解。 显示英文摘要

This paper proposes a novel non-terrestrial networks (NTNs) system that integrates optical intelligent reflecting surfaces (OIRS) and simultaneous transmitting and reflecting Intelligent reflecting surfaces (STAR-IRS) to address critical challenges in next-generation communication networks. The proposed system model features a signal transmitted from the optical ground station (OGS) to the earth station (ES) via an OIRS mounted horizontally on a high altitude platform (HAP). The ES uses an amplify-and-forward (AF) relay with fixed gain for signal relaying, which is then transmitted through a STAR-IRS vertically installed on a building to facilitate communication with both indoor and outdoor users. The FSO link incorporates (multiple-input multiple-output) MIMO technology, and this paper develops a channel model specifically designed for scenarios where the number of OIRS units exceeds one. For the radio-frequency (RF) link, a novel and highly precise approximation method is introduced, offering superior accuracy compared to traditional approaches based on the central limit theorem (CLT). Closed-form analytical expressions for key performance metrics, including outage probability (OP), ergodic capacity and average bit error rate (BER) are derived in terms of the bivariate Fox-H function for this novel five hops system. Asymptotic expressions at high SNR are also presented, providing insights into system diversity order.

替换提交 (展示 1 之 1 条目 )

[2] arXiv:2508.02729 (替换) [中文pdf, pdf, 其他]

标题： 使用深度学习解释性能曲线 显示英文标题

标题： Interpreting Performance Profiles with Deep Learning

Zhuoran Liu

评论： 计算机科学硕士论文，北卡罗来纳州立大学，2022年。导师：刘旭博士

主题： 软件工程 (cs.SE) ; 人工智能 (cs.AI) ; 性能 (cs.PF)

性能分析工具（也称为分析器）在理解运行时程序性能方面起着重要作用，例如热点、瓶颈和低效之处。 虽然分析器已被证明是有用的，但它们给软件工程师带来了额外的负担。 软件工程师作为用户，负责解释复杂的性能数据并识别程序源代码中的可操作优化。 然而，对于用户来说，将低效之处与程序语义联系起来可能具有挑战性，特别是如果用户不是代码的作者，这限制了分析器的适用性。 在本论文中，我们探索了一种新的方向，即结合性能分析和程序语义，并采用深度学习方法。 核心思想是提取代码摘要以获取语义信息（在一定层次上），并将其集成到分析器中，从而更好地理解程序低效之处以进行可操作的优化。 具体来说，我们将由Async Profiler（最先进的Java分析器）生成的分析结果与微调的基于CodeBERT的代码摘要相结合。 我们在图形用户界面中展示了任何选定调用路径的代码摘要。 我们的系统可以有效协助许多Java基准测试的分析。 显示英文摘要

Profiling tools (also known as profilers) play an important role in understanding program performance at runtime, such as hotspots, bottlenecks, and inefficiencies. While profilers have been proven to be useful, they give extra burden to software engineers. Software engineers, as the users, are responsible to interpret the complex performance data and identify actionable optimization in program source code. However, it can be challenging for users to associate inefficiencies with the program semantics, especially if the users are not the authors of the code, which limits the applicability of profilers. In this thesis, we explore a new direction to combine performance profiles and program semantics with a deep learning approach. The key idea is to glean code summary for semantic information (at a certain level) and integrate it into a profiler, which can better understand program inefficiencies for actionable optimization. To be concrete, we combine profiles generated by Async Profiler (the state-of-the-art Java profiler) with code summarization from a fine-tuned CodeBERT-based model. We demonstrate the code summaries of any selected call path in a graphic user interface. Our system can effectively assist analysis on many Java benchmarks.