Angular-Based Hybrid Beamforming for Wideband THz Massive MIMO Systems: Mitigating Beam Split by Leveraging Angular Spread
Bibliographic record
Abstract
Beam split is a critical challenge in wideband THz massive MIMO systems, arising from frequency-dependent beam misalignment that degrades communication performance, particularly in scenarios with narrow beamwidths and large arrays. This work proposes an angular-based hybrid beamforming framework that leverages angular spread to mitigate the beam split effect. Instead of relying on precise angular spread modeling, we utilize coarse angular information to guide the design of subcarrier-specific beams, effectively reducing misalignment across subcarriers. By broadening the effective beamwidth through angular spread, the proposed method enhances user coverage and alleviates beam split without requiring complex time-delay units or hardware-intensive solutions. Simulation results demonstrate that the proposed approach achieves significant improvements in spectral efficiency and beamforming accuracy while maintaining low computational and hardware complexity. This work provides a practical and efficient solution for addressing beam split in next-generation wideband THz communication systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".