A new approach to diversity and multiplexing gains for wideband MIMO channels
Bibliographic record
Abstract
In this paper, a new approach to improving reliability and bandwidth efficiency in communications over frequency-selective channels using multiple antennas is proposed. In the heart of the new approach is a novel space-time orthogonal frequency division multiplexing (OFDM) scheme. The proposed space-time OFDM modulator translates a multiple-input multiple-output (MIMO) channel into a single-input multiple-output (SIMO) channel without the loss of system freedom (the available diversity gain). This translation simplifies code design as compared to that in the conventional MIMO OFDM approach. Instead of more complicated space-time codes, codes that are designed for single-input fading channels can be used with the proposed space-time modulation. For bandwidth-efficient applications, a channel multiplexing scheme is developed to work with the space-time modulator. Unlike the conventional spatial multiplexing schemes, an arbitrary number of data streams can be created and each layer occupies all the transmit antennas all the time. As a result, all the available degrees of freedom are preserved for each layer and a full range of optimal tradeoffs between data rate and reliability is possible. Several examples are given to demonstrate the advantages of the proposed approach over the conventional MIMO OFDM approach
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".