Modeling and Analysis of Coverage in Wideband Sub-Thz Multi-Carrier Systems with Beam-Squint
Bibliographic record
Abstract
This paper studies the effect of beam-squint on the coverage performance in wideband sub-Terahertz (sub-THz) multi-carrier systems from a system-level analysis perspective. Beam-squint, a frequency-dependent beam misalignment, intensifies in wideband systems, reducing beamforming accuracy and overall network performance. To address this issue, we use tools from stochastic geometry and provide an analytical framework to investigate the coverage probability performance of sub-THz networks under the effect of beam-squint. Our framework integrates important system parameters, such as the spatial deployment of base stations (BSs), system transmission bandwidth, transmit and receive antenna array sizes, channel propagation conditions, and blockage impacts. Using numerical and Monte Carlo simulations, we validate our framework's accuracy and highlight the critical impact of beam-squint in constraining the performance of wideband sub-THz networks. The findings reveal that in wideband multi-carrier systems, coverage performance declines as subcarrier frequencies diverge further from the center frequency due to the beam-squint effect. Additionally, the results highlight that although larger antenna arrays improve the coverage performance, their benefits diminish at higher subcarrier frequencies. This is due to reduced beamwidth, which makes the communication link more susceptible to beam-squint effects, ultimately degrading system performance. These insights are valuable for optimizing sub- THz network parameters to mitigate beam-squint's adverse effects and enhance overall network performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".