Localization Error Bounds For 5G mmWave Systems Under Hardware Impairments
Bibliographic record
Abstract
Location-awareness is expected to be one of the main services in 5G millimeter-wave (mmWave) communication systems. In mm-Wave, multiple-input multiple-output (MIMO) systems will be used, leading to the deployment of antenna arrays in both transmitter and receiver. Hardware components being used in transceiver are commonly modeled as linear filters; but practically, this linearity is not fully satisfied. Power amplifiers and filters applied in antennas mostly show nonlinear behavior, causing loss in spectral efficiency (SE) and signal quality. This non-linearity is referred to hardware impairments (HWIs). Under HWIs model at both the transmitter and receiver, 2D localization performance is examined. Towards that, we derive position and orientation error bounds and study the effect of HWIs on the derived bounds. The numerical results reveal that HWIs have a significant effect on localization and it causes more than 100% degradation in both the transmitter and receiver. Also, the rate of degradation stays the same for both position and orientation error bounds except for the oriented UE.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".