Error Bounds for Localization in mmWave MIMO Systems: Effects of Hardware Impairments Considering Perfect and Imperfect Clock Synchronization
Bibliographic record
Abstract
Localization demands high-accuracy positioning, and this rings especially true in the context of fifth-generation (5G) millimeter-wave (mmWave) systems. However, it is easier said than done. mmWave systems require a large number of antennas to be deployed at the transceiver, so having ideal hardware components at each antenna is unrealistic. Degradation in the received signal, caused by hardware impairments (HWIs), affects the spectral efficiency, which in turn influences user positioning. Moreover, a high level of clock synchronization between the base station (BS) and the user equipment (UE) is rarely achieved. In this article, we investigate the effect of HWIs on UE localization under synchronous and asynchronous conditions. In order to minimize imperfect synchronization, two anchors or two-way localization protocols, a round-trip (RLP) as well as a collaborative localization protocol (CLP) are used. Conducting the localization process using the BS, we find the position and orientation bounds. We then study the effect of HWIs on the error bounds under the mentioned scenarios. Our numerical results show that HWIs have a significant impact on localization in all conditions, localization using two anchors and the CLP being more robust, however, against HWIs. Based on our outcome, compensating for imperfect synchronization using RLP does not increase the resilience of the system against 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".