Hybrid Joint Transmission and Coordinated Beamforming in Millimeter-Wave Cellular Networks
Bibliographic record
Abstract
The downlink performance of a millimeter-wave cellular network comprising multiple-antenna-remote radio heads (RRHs) that service users under a hybrid Joint Transmission and Coordinated Beamforming (JT-CB) scheme is analyzed. Using the tools of stochastic geometry, the signal-to-interference-plus noise ratio (SINR) distribution at a typical user is derived and analytical expressions are provided for the coverage probability and network area spectral efficiency (ASE) under the proposed scheme. In addition, the average rate gain under the proposed scheme is characterized as a function of the network load, file request and caching distributions, and the spatial distribution of the RRHs. Numerical results reveal up to 68% coverage probability gain under the hybrid JT-CB scheme, compared to a CB-only scheme in the low coverage regime (>10 dB SINR threshold), while a CB-only scheme outperforms the hybrid JT-CB scheme by up to 5% in the high coverage regime (<;10 dB SINR threshold). Moreover, the hybrid scheme is shown to outperform a JT-only scheme in all coverage regimes, attaining 52% at 30 dB SINR threshold. In addition, up to 25%, 35%, and 120% gains in ASE are shown to be achieved, compared to the JT-only, CB-only, and non-coordinating schemes, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".