Localization Sensitivity Under RSSI Quantization
Bibliographic record
Abstract
Received Signal Strength Indication (RSSI) is a notoriously noisy metric, yet attractive for localization purposes. We study the effect of reducing the RSSI value to a single bit, essentially turning the RSSI measurement into a proximity indicator. We consider systems where a device, that needs to be localized, transmits and the transmission is received by a number of receivers placed at fixed and known locations. We consider two modes of 1-bit quantization: one (global) where the quantization is defined uniformly across all signal receivers, and one (local) where the RSSI is quantized separately for each receiver. We compare the effects of 1-bit quantization across three profiling k-NN-based localization algorithms, comparing them with each other in addition to comparing against results when no quantization is performed. We furthermore consider the case of global 2-bit quantization. Our study is based on profiling data collected before and after modifications were performed to the profiled environment, allowing us to also study the impact of those changes. Our results show comparable performance between no quantization and local 1-bit quantization, and similar results for global 2-bit quantization.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".