Hardware-Aware Joint Localization-Synchronization and Tracking Using Reconfigurable Intelligent Surfaces in 5G and Beyond
Bibliographic record
Abstract
This work investigates joint localization-synchronization and tracking in 5G and beyond. In particular, we target realistic circumstances where the theoretical assumptions of a perfect synchronous system and ideal transceivers no longer exist. We take a close look at the multiple-input single-output (MISO) millimeter-wave (mmwave) system that employs the orthogonal frequency-division multiplexing (OFDM), with the existence of the reconfigurable intelligent surface (RIS). Given the known positions of the RIS and the base station (BS), the single antenna mobile station (MS) can estimate its position and jointly synchronize itself with the multiple antennas BS. This can be accomplished using a maximum likelihood estimator (MLE) whose cost function accounts for the transceivers’ hardware impairments (HWIs). In our tracking scenario, the Kalman filter based tracker (KFT) follows the MLE by paying attention to HWIs-driven accuracy degradation. We then present the theoretical bounds of the tracking accuracy, expressed in terms of the Bayesian Cramer-Rao bound (BCRB). Finally, we conduct computer simulations to demonstrate the adverse deterioration in the joint localization-synchronization process accuracy as well as the accuracy of tracking due to the HWIs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".