Performance Analysis of 6G Communication Links in the Presence of Phase Noise
Bibliographic record
Abstract
As the infrastructure of fifth generation (5G) is integrated worldwide, sixth generation (6G) of cellular communications standard is being developed as the next generation of high-speed wireless communications and internet connectivity. The goal of most innovations in communications is higher data rates and reduced latency. Thus, 6G is envisioned to operate on terahertz (THz) frequencies to leverage wide bandwidth of spectrum. Hardware operating at such high frequencies will be more susceptible to phase noise, or jitter in the time domain, because every time the frequency of the clock is upconverted, the phase noise increases. In addition, 6G will use higher-order modulation schemes to transmit data at higher speeds. It is unknown what modulations 6G communication systems will use, but 5G uses up to 256 QAM when connections are strong, thus we can assume 6G will possibly go even higher than that. Higher data rates require higher signal-to-noise ratios to reduce the bit error rate. Thus the higher the modulation order, the greater the impact of phase noise. The purpose of this paper is to further understand how 6G communication links will be impacted by phase noise. Simulation results demonstrate that there is a significant performance degradation due to phase noise when moving from 5G (Sub-6 GHz and millimeter Wave) to possible 6G carrier frequency ranges and adopting higher order modulation techniques.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".