On the performance of spatial multiplexing MIMO cellular systems with adaptive modulation and scheduling
Bibliographic record
Abstract
We analyze the forward link spectral efficiency (SE) of a spatial multiplexing cellular MIMO system using adaptive modulation and scheduling (opportunistic and proportional fair). With the channel state information (CSI) available only at the receiver side, the minimum mean square error (MMSE)-based ordered successive interference cancellation is employed for detection with either forward or reverse ordering. When the channel state information (CSI) is available at the transmitter, separate channels are obtained via singular value decomposition (SVD) of the channel matrix. The post processing SNR for each stream is fed back to the transmitter to adapt the modulation level corresponding to each stream. The multi-user diversity gain due to scheeduling is observed to be very significant especially without power control. The SE gain from the SVD scheme becomes negligible in a high SNR region which would not justify the complexity of having the CSI at the transmitter. The proportional fair scheduling is a good choice to compromise SE and fairness when the average channel conditions of users are different.
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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 | 0.002 | 0.009 |
| 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) | 0.001 | 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".