Error Bounds for 3D Localization and Maximum Likelihood Estimation of mm-Wave MISO OFDM Systems in the Presence of Hardware Impairments
Bibliographic record
Abstract
Millimeter-wave ($mm$-wave) multiple-input single-output (MISO) systems are expected to be extremely advantageous for the fifth generation (5G) cellular systems. In fact, these systems are considered key enablers of centimeter-level localization accuracy, even in the case of a single base station (BS). However, there are still fundamental issues that need to be addressed when applying$mm$-wave MISO systems to practical scenarios, namely the effects of hardware impairments (HWIs). In this study, the 3D localization accuracy of$mm$-wave MISO OFDM systems is investigated when there are HWIs at both the BS and the mobile station (MS). The localization is performed by estimating the downlink channel parameters of the line-of-sight (LOS) path using only a maximum likelihood (ML) estimator at the MS. We then transform these channel parameters into ones for localization. The Fisher information matrix (FIM) is employed to assess the accuracy of the estimation processes, considering also any non-LOS (NLOS) paths. The limit of localization is calculated in terms of the position error bound (PEB). Computer simulations demonstrate the destructive impacts of HWIs on the localization process. Moreover, it was proven that the effect of NLOS paths from unknown scatters on the localization process is related to the ratio between LOS and NLOS path gains.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".