A Sub-Terahertz Sliding Correlator Channel Sounder with Absolute Timing using Precision Time Protocol over Wi-Fi
Bibliographic record
Abstract
Radio channels at mmWave and sub-THz frequencies for 5G and 6G communications offer large channel bandwidths (hundreds of MHz to several GHz) to achieve multi-Gbps data rates. Accurate modeling of the radio channel for these wide bandwidths requires capturing the absolute timing of multipath component (MPC) propagation delays with sub-nanosecond accuracy. Achieving such timing accuracy is challenging due to clock drift in untethered transmitter (TX) and receiver (RX) clocks used in time-domain channel sounders, yet will become vital in many future 6G applications. This paper proposes a novel solution utilizing precision time protocol (PTP) and periodic drift correction to achieve absolute timing for MPCs in power delay profiles (PDPs) --captured as discrete samples using sliding correlation channel sounders. Two RaspberryPi computers are programmed to implement PTP over a dedicated Wi-Fi link and synchronize the TX and RX Rubidium clocks continuously every second. This synchronization minimizes clock drift, reducing PDP sample drift to 150 samples/hour, compared to several thousand samples/hour without synchronization. Additionally, a periodic drift correction algorithm is applied to eliminate PDP sample drift and achieve sub-nanosecond timing accuracy for MPC delays. The achieved synchronicity eliminates the need for tedious and sometimes inaccurate ray tracing to synthesize omnidirectional PDPs from directional measurements. The presented solution shows promise in myriad applications, including precise position location and distributed systems that require sub-nanosecond timing accuracy and synchronization among components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".