Net Insight develops network timing and synchronization equipment for mobile networks. One of our products, Zyntai, provides GNSS-independent time synchronization for 5G using time transfer. This thesis would be carried out together with our R&D team in Stockholm and focus on precision timing systems. The problem 5G networks need base stations to stay synchronized to within about a microsecond, measured as time error (TE). Too much drift between nearby base stations causes interference and degrades the network. Base stations normally get this accuracy from GNSS, but GNSS can be jammed, spoofed, or lost, and backup synchronization via time transfer isn't always available either. When that happens, equipment falls back on holdover, using its own oscillator to keep time. Oscillators that seem accurate by human standards drift into unacceptable TE within a few hours, and extending that window without resorting to an expensive, higher quality oscillator is still an open industry problem. There is no fixed answer for how to approach it. The problem is wide and can be tackled in different ways. A few directions worth looking into: Kalman filter based prediction of oscillator drift, switching more smoothly between trusting the network and holding the current time, machine learning to let the system learn an oscillator's behavior from data directly or combining information from more than one frequency source. Other creative approaches are welcomed as well! Ideally, the thesis will implement and evaluate multiple approaches. Which of these to focus on is something we would decide together before starting the thesis work. Why do your thesis with Net Insight? Supervision from engineers who work on this hardware and software every day. A desk with the team at our Stockholm office, access to our lab equipment and test setups, the real gear we test our own products with. You’ll work with a real-world problem nobody's fully solved yet, contributing to a cutting-edge high-precision timing solution. We’re looking for curious minds with a solid technical foundation and a passion for problem-solving! Check out this video where previous master thesis students, now proud employees, share their experience of doing their master thesis with us. Your education is ideally in electrical engineering, engineering physics, computer science, or an equivalent field, ideally with a background in control theory, C++, Python, Linux, networking, and embedded systems. This project is suitable for 1-2 students. Ready to turn your thesis into something truly impactful? Join us at Net Insight and let’s create the future of synchronization together! Further reading 5G uses time division duplexing, alternating between sending and receiving on the same frequency in windows often below one millisecond. This requires base stations to agree on time to within microseconds: if nearby base stations become sufficiently out of sync, their transmissions interfere, degrading service or causing outages. A 5G base station is considered out of sync once its time error (TE) exceeds 1 µs. Base stations obtain this accuracy from GNSS satellites, which distribute reference time established by timing laboratories using atomic clocks. This has long been the industry standard, but GNSS availability cannot be guaranteed: its signals can be jammed or spoofed, making timing unreliable or unavailable in some regions. When the primary reference is lost, equipment enters holdover, maintaining synchronization using its own local frequency source until the reference returns. This source is typically an oscillator (TCXO/OCXO) rather than an atomic clock. Such oscillators are highly accurate by human standards, drifting only milliseconds per year, but this small drift becomes significant at microsecond scale: extrapolated to the 1 µs threshold, such an oscillator may keep equipment within limits for only around 3-5 hours. Enhanced holdover studies how timing equipment can remain within required synchronization limits while its primary reference is unavailable. The problem is not simply keeping a local clock running, but extending the time before accumulated error makes equipment unusable for high-precision systems such as 5G base stations. Oscillators and frequency stability Timing equipment uses a physical oscillator as its frequency reference. Common types include quartz crystal oscillators, temperature-compensated (TCXO) and oven-controlled (OCXO) variants for higher stability, and atomic references (rubidium, cesium) where extreme stability is required. No oscillator runs at exactly its nominal frequency; the deviation is specified as accuracy or stability, typically in parts per million (ppm) or parts per billion (ppb). Long-term average accuracy and short-term stability (how much frequency wanders moment-to-moment) are related but distinct, and it is short-term stability that dominates holdover performance. From ppm to microseconds A relative frequency error accumulates a time error over time when in holdover. An oscillator good enough to hold 1 µs of error for three hours has a stability of approximately 1 µs ÷ 10,800 s ≈ 9.3×10⁻¹¹: about 1 part in 10 billion. The same stability implies roughly 8 µs of drift per day and about 3 milliseconds per year, which illustrates the core tension: a level of accuracy no human would ever notice is still insufficient for microsecond-scale radio synchronization by several hours' margin. The time-error budget and TDD synchronization Time Error (TE) is the difference between a node's output time and true reference time, typically reported as a worst-case value over an observation window. TDD networks require tight TE control because neighboring base stations share the same frequency channel and time-multiplex uplink and downlink transmissions; misaligned frame boundaries beyond the network's error budget cause uplink and downlink windows to overlap between cells, producing interference. ITU-T G.8271 formalizes this into accuracy classes, with ±1.5 µs covering standard LTE-TDD/NR-TDD deployments and ±1 µs and below applying to some configurations and more advanced coordinated radio features. This is an end-to-end budget shared across the full delivery chain from the primary reference to the radio unit, so a node's own holdover error draws directly on the same margin consumed by every other stage of the chain. Relevant links Time transfer: https://en.wikipedia.org/wiki/Time_and_frequency_transfer Frequency standard: https://en.wikipedia.org/wiki/Frequency_standard - OCXO: https://en.wikipedia.org/wiki/Crystal_oven - Rubidium standard: https://en.wikipedia.org/wiki/Rubidium_standard - Caesium standard: https://en.wikipedia.org/wiki/Caesium_standard - Hydrogen maser: https://en.wikipedia.org/wiki/Hydrogen_maser GNSS disciplining: https://en.wikipedia.org/wiki/GPS-disciplined_oscillator Adaptive drift compensation for holdover oscillators: https://www.sitime.com/api/gated/Adaptive-Drift-Compensation-for-Holdover-Oscillators.pdf Veritasium video on GNSS jamming: https://www.youtube.com/watch?v=tz23G_UXCGA
