Error Analysis of the Joint Localization and Synchronization of RIS-Assisted <i>mm</i>-Wave MISO-OFDM Under the Effect of Hardware Impairments
Bibliographic record
Abstract
This work investigates the theoretical bounds of the joint localization and synchronization processes in a reconfigurable intelligent surface (RIS)-assisted system. We address the case of millimeter-wave (mm-Wave) multiple-input single-output (MISO) orthogonal frequency-division multiplexing (OFDM) with non-ideal transceivers. Considering a single antenna mobile station MS aims to estimate the parameters of the downlinks from the base station (BS) and the RIS by observing a known sequence received by the MS directly from the BS and indirectly through the RIS. The theoretical bounds of the estimation process are assessed by using the Fisher information matrix (FIM). A transformation matrix is then used to convert the FIM of the downlink channel parameters to the FIM of the MS joint localization and synchronization parameters. Specifically, the transformation matrix is derived based on the geometric relationships that convert the estimated downlink channels’ parameters to the position coordinates and clock offset. Next, the Cramer-Rao lower bound (CRLB) matrix of the joint localization and synchronization process is obtained by using the pseudo-inverse of the FIM. Thus, the position error bound (PEB), as well as the synchronization error bound (SEB), are calculated. Computer simulation results are provided to illustrate the adverse effects of the HWIs on the accuracy of localization and synchronization. These results are given in proportion to the effective signal-to-noise ratio (SNR), the number of pilot transmissions, and the number of the RIS elements.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".