5G-SRNG：面向低熵源设备的基于5G频谱图的随机数生成
5G-SRNG: 5G Spectrogram-based Random Number Generation for Devices with Low Entropy Sources
AI总结：
本文提出5G-SRNG，利用5G频谱信息作为熵源，为低熵源设备提供端到端随机数生成方案，实验表明其在随机性、相关性和功耗上均优于现有方法。
摘要：
随机数生成（RNG）是安全协议中的关键要素，其性能和可靠性对数字系统的安全性与完整性至关重要。在拥有大量低熵源设备的5G网络中，这一点尤为突出。本文提出了5G-SRNG，一种面向5G网络中低熵源设备的端到端随机数生成解决方案。与传统RNG方法相比，5G-SRNG不依赖于硬件或软件随机数生成器，而是利用5G频谱信息（例如来自频谱感知或频谱感知反馈机制的信息）作为熵源。所提算法经过实验验证，并通过模拟真实的5G网络环境对其性能进行了分析。结果表明，5G-SRNG在随机性、部分相关性和功耗等所有方面均优于现有RNG，使其适用于5G网络部署。
英文摘要：
Random number generation (RNG) is a crucial element in security protocols, and its performance and reliability are critical for the safety and integrity of digital systems. This is especially true in 5G networks with many devices with low entropy sources. This paper proposes 5G-SRNG, an end-to-end random number generation solution for devices with low entropy sources in 5G networks. Compared to traditional RNG methods, the 5G-SRNG relies on hardware or software random number generators, using 5G spectral information, such as from spectrum-sensing or a spectrum-aware feedback mechanism, as a source of entropy. The proposed algorithm is experimentally verified, and its performance is analysed by simulating a realistic 5G network environment. Results show that 5G-SRNG outperforms existing RNG in all aspects, including randomness, partial correlation and power, making it suitable for 5G network deployments.
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。
