RIS-Aided Mobile Localization Error Bounds Under Hardware Impairments
Bibliographic record
Abstract
Reconfigurable intelligent surfaces (RISs) are a recent yet revolutionary development in communications systems. Particularly applicable to millileter wave (mmWave) systems, these surfaces can increase localization performance and decrease vulnerability to environmental influences, all by adjusting the incoming signals’ phase. At the same time, manufacturing ideal hardware to be deployed at the transceivers is not feasible nor practical. These non-linearities in hardware, collectively known as hardware impairments (HWIs), cause signal degradation and adversely affect localization. In this paper, the effect of HWIs on RIS-aided localization is examined. Towards that, the mean squared error (MSE) of the user’s position is found through a maximum likelihood estimator (MLE) and its functionality is verified by the position error bounds (PEB), derived from Cramér-Rao lower bounds (CRLB). Our numerical results show that active RISs mitigate the deteriorating effect of HWIs on the user’s PEB. Based on our outcome, increasing the inter-RISs space generally creates more resolvable paths and leads to improved localization.
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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 | 0.002 | 0.016 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".