Comparison of capacities of the transmit antenna diversity with the receive antenna diversity in the MIMO scheme
Bibliographic record
Abstract
It is well known that spectral efficiency can be dramatically increased by employing multiple transmit and receive antennas. This multi-element technology processes the spatial dimension to improve wireless capacities. Since there are multiple antennas in both transmit and receive sides, a natural question is whether more antennas should be at the transmitter or the receiver in order to achieve greater capacity? A second question is how many antennas should be deployed in the transmitter and the receiver to make the channel capacities reach their saturation level? These two questions are important to the optimization of multiple input multiple output transmission schemes. To address these questions, we explore the important case of when the channel characteristic is known at the receiver. The analytical model assumes that the paths between antennas fade independently. Further, the channel is assumed to be fixed during a burst and to change randomly from burst to burst. To answer the first question, we use the outage capacity complementary cumulative distribution function to show that the capacity with more receive antennas and fewer transmit antennas is higher than the opposite configuration. To answer the second question, we use numerical simulation to explore the saturation points of the capacities for different signal-to-noise ratios. These simulations give results illustrating the relations among the saturation level capacity, antenna number and signal-to-noise ratio.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".