Heterogeneous multiuser transmission with BEM-based limited feedback over doubly selective MIMO downlink channels
Bibliographic record
Abstract
This paper studies the problem of limited feedback design for heterogeneous multiuser transmissions over time- and frequency-selective (doubly selective) multiple-input multiple-output (MIMO) downlink channels. In limited feedback design over the channels, the discrete prolate spheroidal basis expansion model (DPS-BEM) is used as a fitting parametric model for capturing the time-variation of the multiuser downlink channels and for reducing the number of the channel parameters. The resulting dimension reduction in the channel representation, in turn, translates into a reduced feedback load of channel state information (CSI) to the base station (BS). To exploit the considerable reduction in CSI feedback load, vector quantization (VQ) of the DPS-BEM coefficients is performed at users under the assumption that perfect BEM coefficient estimation has been established by existing algorithms. The output indices of the quantized BEM coefficient vectors are assumed to be sent to the BS via error-free, zero-latency feedback links. With the quantized CSI, the block-diagonalization (BD) precoding and greedy scheduling techniques are employed for the heterogeneous multiuser transmission (i.e., users with different numbers of receive antennas and different signal-to-noise ratios). Numerical results show that the BEM-based limited feedback scheme is able to significantly alleviate the detrimental effect of outdated CSI feedback over the time-varying channels.
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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".