Information rates for multi‐dimensional modulation over multiple antenna wireless channels
Bibliographic record
Abstract
Abstract This paper considers achievable information rates for space‐‐time modulation schemes created by various allocations of signal dimensions to transmit antennas, and various transmit power allocations. We employ a deterministic space and time dispersive channel model following the 3rd Generation Partnership Project (3GPP) standards. With informed transmitters, Shannon capacity is achieved by a water‐filling power allocation and an eigen‐beamforming signalling structure. With uninformed transmitters, we represent the lack of channel knowledge by an a‐prior probability distribution on the components of the channel propagation matrix. Then we show that a uniform power allocation makes the fraction of channels whose mutual information is less than any given rate, converge to zero fastest as the a‐prior distribution becomes non‐informative. Allocating all signal dimensions to all transmit antennas has significant benefits in many situations when the signal‐to‐noise ratio (SNR) is not too small. For extreme SNR (low and high) cases, we consider power and signal dimension allocations that maximise the information rate with partial transmitter channel knowledge. Copyright © 2008 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".