Doppler Spread Suppression Technique for an L-Band Digital Radio Broadcast System
Bibliographic record
Abstract
The orthogonal frequency division multiplex (OFDM), which is at the heart of many digital broadcasting and wireless standards, is very sensitive to Doppler spread induced by time variations of the mobile channel. This undesirable effect can be particularly detrimental to system performance when such a system is used for vehicular reception at high frequency bands since the maximum Doppler frequency fdmaxis proportional to the radio frequency of the received signal and vehicle speed. In this paper we propose to study the performance of a dual-antenna Doppler spread mitigation technique applied to a mobile digital radio broadcast system (Canadian DAB system in mode IV). This technique uses a linear antenna array, parallel to the direction of motion of the vehicle, and estimates the received signal at a virtual point by using space domain minimum mean square error (MMSE) type interpolation. Laboratory test results show that this scheme can effectively reduce bit error degradations caused by the spread of the Doppler spectrum at high vehicle speeds. Field tests have been performed and analysis of the collected data is underway to validate the proposed technique for an L-band DAB system.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".