BEM-Based Limited Feedback for Precoding and Scheduling over Doubly Selective Multiuser MISO Downlink Channels
Bibliographic record
Abstract
This paper studies the problem of scheduling, preceding and limited feedback design for the emerging 3GPP-LTE systems over time-and frequency-selective (doubly selective) channels. In particular, greedy scheduling with zeroforcing (ZF) precoding is considered for the doubly selective multiuser multiple-input single-output (MISO) orthogonal frequency division multiplexing (OFDM) downlink channels. In limited feedback design, the discrete prolate spheroidal basis expansion model (DPS-BEM) is used as a fitting parametric model for capturing the time-variation of the doubly selective channels and 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 exploit the considerable reduction in CSI feedback load, vector quantization (VQ) of DPS-BEM parameters is performed at users' receivers under the assumption that perfect BEM parameter estimation has been established by existing algorithms. The output indices of the quantized BEM parameter vectors are, then, sent to the base station (BS) via error-free limited feedback links. With the channel state information (CSI) at transmitter (CSIT), greedy scheduling and ZF precoding are deployed for multiuser transmission in each subcarrier of OFDM symbols in a LTE frame. Numerical results show that the ZF-based multiuser transmission scheme with the suggested BEM quantization and limited feedback design offers significant sum-rate gains and stable performance with high robustness against time-varying channels.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".