Covert Localization in Wireless Networks: Feasibility and Performance Analysis
Bibliographic record
Abstract
In this paper, we propose covert localization to improve the security of wireless localization networks, which can prevent the legitimate transmission of localization signals between anchors and agent from being detected by the illegitimate warden. Specifically, we first establish a framework of covert localization and demonstrate its feasibility when the warden suffers noise uncertainty. Then, with two specific noise uncertainty distributions, we derive the fundamental limit of localization accuracy, i.e., covert squared position error bound (CSPEB), which is the achievable localization accuracy for the agent while ensuring covertness for the warden. Theoretical analysis of CSPEB demonstrates the impact of different factors on the localization accuracy. Besides, in an energy-constrained scenario, we formulate a power allocation problem to refine anchors' power to minimize the CSPEB for a given total power budget and develop an algorithm based on the semidefinite program (SDP). Simulation results verify our theoretical analysis by evaluating the effect of several representative factors on the CSPEB and show the superiority of the SDP-based power allocation algorithm to the other baseline methods.
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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 | 0.002 |
| 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) | 0.000 | 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".