Signal detection performance in Rayleigh fading environments with a moving antenna
Bibliographic record
Abstract
The performances of a single-antenna handheld receiver in detecting a narrowband signal in a Rayleigh fading environment that is temporally static but decorrelates spatially are analysed. Of interest is comparing the detection performance of a static antenna with that of a moving antenna subject to constant processing time. It is shown that the net processing gain resulting from randomly moving the antenna relative to keeping it static can be large, namely over 11 dB in some cases, which is significant for numerous indoor applications. It is further demonstrated that, for a given utilisation scenario, there is an optimum number of spatial samples that maximise the processing gain advantage of the moving antenna. Generally, if the spatial trajectory of the antenna becomes too large, then the loss associated with the signal decorrelation dominates and undermines the gains achieved by the increased spatial diversity. Practical implementation issues including the sensitivity of the proposed method to trajectory estimation are investigated. An extensive set of measurements based on CDMA 2000 signals propagated from outdoor terrestrial base stations and captured in indoor multipath environments using static and moving antennas are utilised to experimentally substantiate these theoretical findings.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".