Location Estimates From Channel State Information via Binary Programming
Bibliographic record
Abstract
Recent years have witnessed a marked increase in the efficacy and speed to solution of binary program solvers. Leveraging these developments, we propose converting the sparse modelling problem to a binary program, and we derive the conditions under which such a conversion yields the optimal signal support. Furthermore, we derive an upper bound on this condition, which we can minimize by designing a structured tight frame as the dictionary. We apply these theoretical insights to binary optimization of angle of arrival and time of flight estimation (BOAT) using channel state information (CSI), with the goal of localizing a transmitter in an indoor multipath-rich environment. Finally, we show how a covariance array processing approach, together with the novel binary programming paradigm, results in a system that outperforms two state-of-the-art solutions. Our claims are substantiated using both simulated data and experimental data collected using “off-the-shelf” WiFi IEEE 802.11ac routers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".