Two Novel Channel-Augmentation Schemes for MIMO Systems
Bibliographic record
Abstract
Recently, Rankin, Taylor, and Martin (RTM) have proposed a simple channel-augmentation scheme for multiple-input multiple-output (MIMO) systems. They have shown that the virtual receive antennas created by repeating the same transmit symbols several times can improve the outage rate of slow MIMO fading channels, especially if the number of transmit antennas is smaller than the number of receive antennas. Inspired by the RTM paper, we propose in this paper two channel-augmentation schemes having the same low complexity as the RTM scheme. In the first, the transmitted symbols are both repeated and complex conjugated leading to the conjugate virtual antenna (CVA) scheme. In the second, the transmitted symbols are constrained to be real-valued leading to the real virtual antenna (RVA) scheme. We show that the RTM, CVA, and RVA schemes can achieve a higher outage rate than the baseline scheme without augmentation for both V-BLAST and D-BLAST. The novel RVA and CVA schemes are seen to be more robust to channel correlation than the RTM scheme.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".