Robust multiuser MIMO transceiver design under channel uncertainty
Bibliographic record
Abstract
Employing multiple antennas in multiuser communication systems has the potential to increasing the achieved data rates and improving the overall system performance. However, many of these potential gains depend on the amount of channel state information available (CSI) at both the transmitter and the receiver. In many systems, the CSI that is available at the transmitter suffers from inaccuracies that are caused by errors in channel estimation and/or limited delayed or erroneous feedback, and the performance of many multiuser systems are particularly sensitive to uncertainties in the CSI. These uncertainties can result in multiuser systems that are dominated by interference, and hence in a significant degradation of the quality of service (QoS) offered to the users and their data rates. Due to the inevitability of imperfect CSI, robust communication schemes that take into account the channel uncertainty are of interest in practice. The goal of the tutorial is to provide a unified exposition of the theoretical results regarding efficient design of multiuser systems that explicitly take into account these uncertainties. This unification is provided throggh the theories of robust and convex optimization It will be demonstrated that by incorporating robustness in the design one can significantly reduce the sensitivity of multiuser systems to channel uncertainties and mitigate their deleterious effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".