SDR Driver for Precise Timing Applications

AI-assisted generation of a SoapySDR-compatible driver for the HackRF One, enabling synchronized sampling and clock-frequency control

Fabrizio Pollastri· 2026· DOI 10.48550/arXiv.2608.23614

The core problem

Software-defined radio (SDR) platforms have become increasingly accessible, with low-cost devices such as the HackRF One enabling a wide range of experimentation. However, many precise timing applications—including synchronized sampling, time-scale control, and clock-frequency adjustment—are not natively supported by the original firmware. This work addresses that gap by extending the HackRF One firmware with timing functions and developing software drivers that expose these capabilities to users. The paper describes two driver implementations: an initial Python wrapper and a subsequent SoapySDR-compatible driver. The latter was produced using an AI-assisted development workflow that leveraged the existing HackRF One and SoapySDR codebases, resulting in a functional driver after only limited debugging. The approach significantly reduced development effort and improved software maintainability.

The AI-assisted development workflow produced a SoapySDR-compatible driver that became operational after only limited debugging. The authors report that this approach reduced development effort by more than an order of magnitude compared to traditional manual development. The resulting driver improves software maintainability, as it conforms to the SoapySDR API and can be integrated with existing SDR frameworks. The initial Python wrapper served as a proof of concept, but the SoapySDR-compatible driver is the primary deliverable. The firmware extensions enable precise timing applications that were previously not possible with the low-cost HackRF One. No quantitative performance metrics (e.g., timing jitter, synchronization accuracy) are provided in the abstract; the key result is the successful generation and operation of the driver with minimal debugging effort.

Software-defined radio (SDR) platforms have become increasingly accessible, with low-cost devices such as the HackRF One enabling a wide range of experimentation. However, many precise timing applications—including synchronized sampling, time-scale control, and clock-frequency adjustment—are not natively supported by the original firmware. This work addresses that gap by extending the HackRF One firmware with timing functions and developing software drivers that expose these capabilities to users. The paper describes two driver implementations: an initial Python wrapper and a subsequent SoapySDR-compatible driver. The latter was produced using an AI-assisted development workflow that leveraged the existing HackRF One and SoapySDR codebases, resulting in a functional driver after only limited debugging. The approach significantly reduced development effort and improved software maintainability.

The development process began with extending the original HackRF One firmware to include timing functions for synchronized sampling, time-scale control, and clock-frequency adjustment. An initial driver implementation was created as a Python wrapper around these firmware extensions. Recognizing limitations in maintainability and integration, the authors redesigned the driver as a SoapySDR-compatible module. This redesign was carried out using an AI-assisted development workflow: the new driver was generated from the existing HackRF One and SoapySDR codebases, with the AI system producing code that required only limited debugging to become operational. The workflow is summarized in the following Mermaid diagram:

The work demonstrates the potential of AI-assisted development for creating specialized SDR drivers. By leveraging existing codebases (HackRF One and SoapySDR), the AI system was able to generate a functional driver with limited debugging, reducing development effort by more than an order of magnitude. This suggests that AI-assisted workflows can significantly accelerate the development of low-level software, particularly when high-quality reference code is available. The SoapySDR compatibility ensures that the driver can be used with a wide range of SDR applications, enhancing its practical utility. However, the abstract does not provide details on the specific timing performance achieved, nor does it discuss potential limitations of the AI-generated code. Future work could involve quantitative evaluation of timing precision and broader testing across different host platforms. The approach also raises questions about the maintainability of AI-generated code over time, though the authors claim improved maintainability relative to the initial Python wrapper.

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