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IEEE Access

High Throughput 512Mbps True Random Number Generation Using Uncertainty in Mobile Communication Bands

Farid Alidoust Aghdam, Mohammad Erfanimehr, Hodjat Ahmadi, Armin Zarghami, Seyed Ahmad Madani, Ali Rostami

Published Jan 1, 2024• 1 citation
Publisher

Abstract

This study presents a new approach to generating true random numbers by exploiting the random nature inherent in mobile communication networks. Taking advantage of fluctuations in total users, user activity rate, and channel fading, we receive a raw signal in the radio spectrum using bowtie microstrip antennas designed for GSM and 5G frequency bands in the 1.8 and 3.5 GHz bands that can be used in our system. The captured data is then processed using a field-programmable gate array to generate high-quality true random numbers. Bow tie antennas are designed to have an omnidirectional pattern and can receive the signal from all directions. We have evaluated this through numerical calculations and antenna pattern measurements in the laboratory, and the results and their comparison show a promising performance. Our random number generation system also includes a Zynq SoC module and a dual-channel AD9226 ADC, enabling real-time random number generation through the ethernet port at up to 512Mbps. To ensure the system’s performance, standard statistical tests, including the NIST, DIEHARDER, and ENT test suites, have been done, and all confirm the true randomness of the generated random numbers. Our findings show the effectiveness of the 3.5 GHz frequency band as a suitable source for generating random numbers, especially in mobile devices and the Internet of Things. Due to the high speed of generating random numbers, this optimal and efficient TRNG system can have broad applications in cryptography, statistical analysis, and computational simulations.

DOI
10.1109/access.2024.3513874

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This study presents a new approach to generating true random numbers by exploiting the random nature inherent in mobile communication networks. Taking advantage of fluctuations in total users, user activity rate, and channel fading, we receive a raw signal in the radio spectrum using bowtie microstrip antennas designed for GSM and 5G frequency bands in the 1.8 and 3.5 GHz bands that can be used in our system. The captured data is then processed using a field-programmable gate array to generate high-quality true random numbers. Bow tie antennas are designed to have an omnidirectional pattern and can receive the signal from all directions. We have evaluated this through numerical calculations and antenna pattern measurements in the laboratory, and the results and their comparison show a promising performance. Our random number generation system also includes a Zynq SoC module and a dual-channel AD9226 ADC, enabling real-time random number generation through the ethernet port at up to 512Mbps. To ensure the system’s performance, standard statistical tests, including the NIST, DIEHARDER, and ENT test suites, have been done, and all confirm the true randomness of the generated random numbers. Our findings show the effectiveness of the 3.5 GHz frequency band as a suitable source for generating random numbers, especially in mobile devices and the Internet of Things. Due to the high speed of generating random numbers, this optimal and efficient TRNG system can have broad applications in cryptography, statistical analysis, and computational simulations.
True random number generators, which output truly random numbers by extracting entropy from physical phenomena such as thermal and electronic noises, are widely used in the field of the cryptographic communication systems. We have been developing a superconductive true random number generator that can generate truly random number sequences, impossible to be predicted, by utilizing the high-speed operation and high-sensitivity of superconductive integrated circuits. In this study, we have calculated the dependences of correlation of output random bits on the generation rate. Statistical tests have been performed on the basis of the NIST statistical test suite in order to evaluate the quality of the randomness of sequences generated by the superconductive true random number generator at high generation rate. We have generated a random number sequence consisting of 3.2 M-bit at the generation rate of 30 Gbit/s using the superconductive true random number generator, fabricated by the ISTEC-SRL 2.5 kA/cm2Nb standard process. The generated random number sequences passed 13 kinds of the statistical tests in the NIST statistical test suit, although the 3 tests were not performed because of the shortage of the generated random numbers. The result sufficiently proves that a superconductive true random number generator can generate a high quality of random numbers that can be used for practical cryptographic applications, at a generation rate of up to 30 Gbit/s.
A comprehensive performance analysis of a TRNG system that is based on beat frequency is provided in this paper for secure, high-entropy applications. The proposed method generates unpredictable bit streams by leveraging the intrinsic unpredictability caused by the frequency difference between two distinct oscillators. The system employs phase noise and variations in the surrounding environment to increase entropy without the need for laborious post-processing procedures. The study evaluates statistical variance using well-known test suites such as the NIST and Diehard tests to ensure high-quality output. The entropy rate, stability, power consumption, and throughput are among the critical performance metrics that are pertinent. The experimental data firmly refutes exogenous influences and deterministic patterns.
Random Number Generators (RNGs) are crucial for applications ranging from cryptography to simulations. Depending on the source of randomness, RNGs are classified into Pseudo-Random Number Generators (PRNGs), True Random Number Generators (TRNGs), and Quantum Random Number Generators (QRNGs). This work presents the end-to-end development of a high-speed, high-efficiency, phase-noise-based QRNG system that taps into the quantum phase noise of a single-frequency laser, with randomness originating from spontaneous emission. Using a self-heterodyne measurement with a semiconductor laser (linewidth ≈\approx≈ 5.23 GHzGHzGHz) operated near threshold and a $\sim$48 cmcmcm fiber delay line, a raw data generation rate of 2.0 GbpsGbpsGbps is achieved. To ensure uniform randomness in the QRNG output, robust extraction techniques developed in-house, such as the Toeplitz Strong Extractor (TSE), are used. Randomness validation using the NIST and Diehard test suites confirms that all statistical tests pass at standard confidence levels. The developed system achieves a post-processed generation rate of 1.0 GbpsGbpsGbps in operation and attains a Technology Readiness Level (TRL) of 7, approaching TRL 8, making it suitable for real-time secure applications such as cryptographic key generation and stochastic modeling.
High-speed true random number generator is a building block in the modern information security system. We propose and demonstrate an efficient high-speed true random number generator based on multiple parallel self-timed rings (STRs). To improve the security, we evaluate the randomness of the entropy source by min-entropy and exploit the information-theoretically provable Toeplitz-hashing extractor. To minimize the consumption of hardware resources of a field programmable gate array at a predetermined high throughput and maximize the throughput with the limited hardware resources, we systematically derive and investigate the dependence of the data throughput and the total consumed resources of the random number generator on the system parameters. On this basis, we make a joint optimization for the degree of parallelism of the STRs and the extraction efficiency of the extractor. A 10-Gbps true random number generator is implemented efficiently, so that the output random bits can pass all the National Institute of Standards and Technology (NIST) and Dieharder test suites.
Random number generation is fundamental for many modern applications including cryptography, simulations and machine learning. Traditional pseudo-random numbers may offer statistical unpredictability, but are ultimately deterministic. On the other hand, True Random Number Generation (TRNG) offers true randomness. One way of obtaining such randomness are quantum systems, including quantum computers. As such the use of quantum computers for TRNG has received considerable attention in recent years. However, existing studies almost exclusively consider IBM quantum computers, often stop at using simulations and usually test only a handful of different TRNG quantum circuits. In this paper, we address those issues by presenting a study of TRNG circuits on Odra 5 a real-life quantum computer installed at Wrocław University of Science and Technology. It is also the first study to utilize the IQM superconducting architecture. Since Odra 5 is available on-premises it allows for much more comprehensive study of various TRNG circuits. In particular, we consider 5 types of TRNG circuits with 105 circuit subvariants in total. Each circuit is used to generate 1 million bits. We then perform an analysis of the quality of the obtained random sequences using the NIST SP 800-22 and NIST SP 800-90B test suites. We also provide a comprehensive review of existing literature on quantum computer-based TRNGs.
Random numbers are used in various domains, including quantum communication and cryptography applications such as key generation and authentication. Quantum mechanics has the intrinsic ability to generate truly random numbers, making it an ideal alternative for scientific applications that require randomness. Quantum wireless communication is proposed as a new means of communication that is safe, secure, and efficient. The simplicity of our source rotation gate, combined with its independently verifiable solitary unpredictability, is essential to obtain quantum random number generators without spending any money. In this chapter, we explore and study the 24-qubit random number. The worst-case entropy value for such randomly produced integers is 0.999445, with the min-entropy of such numbers being 0.0008. Additionally, steering restart tests were used to validate later verification of true randomness, and the statistical characteristics of the True Random Number Generation (TRNG) were assessed using an autocorrelation study and the statistical test suites.