SINR-Based Interleaved Training Design for Multi-User Massive MIMO Downlink with MRT
Bibliographic record
Abstract
An interleaved training scheme is proposed for multi-user massive multi-input-multi-output (MIMO) downlink with maximum-ratio-transmission (MRT). The base station (BS) sends pilots to train the channels antenna-by-antenna and the training steps are interleaved with the feedback of the channel state information (CSI) from the users. For each training step of the interleaved scheme, the BS decides whether to continue or to stop the training process based on the quality-of-service (QoS) provided by the available CSI. The training time and the transmission success rate of the proposed scheme are analyzed with closed-form approximations derived. Simulations show that the proposed scheme can largely save the average training time without sacrificing the QoS of users. The analytical results are also verified via simulation.
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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 | 0.001 | 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".