Joint Scheduling and Precoding for mmWave and Sub-6GHz Dual-Mode Networks
Bibliographic record
Abstract
A millimeter wave (mmWave) and sub-6 GHz (μWave) dual-mode network can take advantages of the signals over both bands. The user scheduling in the medium access layer and the transmit precoding in the physical layer are coupled and should be optimized jointly. This work investigates the joint crosslayer optimization problems for two dual-mode systems: the full dual transmission system in which each user equipment can be scheduled over both frequency bands and the half dual transmission system in which each user equipment can only be scheduled over one band. A concave expression is adopted to lower-bound the achievable rate of each user equipment with perfect and imperfect channel estimation. Based on the lower bound, effective algorithms are proposed to solve the joint cross-layer optimization problems. The proposed algorithms can also be used for other dual-mode networks. Simulations show superiority of joint transmit precoding for the two frequency bands signals. Moreover, the full dual transmission system outperforms the half dual transmission system in terms of the minimum rate. The rate degradation of the half dual transmission system is insignificant while its implementation complexity is much lower than the full dual transmission system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".