On the Capacity of Faster-than-Nyquist MIMO Transmission with CSI at the Receiver
Bibliographic record
Abstract
The demand for mobile data continues to grow as more and more data intensive applications become available to end users. One of the major challenges for the fifth generation (5G) and future mobile standards will be to develop a communication scheme capable of keeping up with the increasing demand for consumer data. In this paper we consider using faster-than-Nyquist (FTN) signaling in multiple-input-multiple-output (MIMO) systems to achieve a significantly increased channel capacity. First, we present two FTN MIMO system architectures: regularly sampled FTN MIMO and orthogonalized FTN MIMO. Then, we derive discrete-time channel models of these two architectures and evaluate the corresponding channel capacity. Our simulated capacity results indicate that a greater channel capacity can be achieved by these two FTN MIMO architectures, which is larger than using either FTN or MIMO technology independently. In addition, the orthogonalized FTN MIMO system can also reduce the complexity of the analog signal chain and de- correlates the noise in the received samples. Therefore, FTN MIMO is a potential technology for future communication systems where high throughputs are required.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".