Low-Complexity Robust MISO Downlink Precoder Design With Per-Antenna Power Constraints
Bibliographic record
Abstract
This paper considers the design of beamformers for a multiple-input single-output downlink system with per-antenna power constraints (PAPCs) that seek to mitigate the impact of the imperfections in the channel state information that is available at the base station. The goal of the design is to minimize the outage probability of specified signal-to-interference-and-noise ratio targets, and to do so at a low computational cost. The proposed design strategy provides an efficient way to handle PAPCs, in addition to a total power constraint, for a variety of precoding techniques, including the offset maximization approach to robust beamforming, and the nominal zero-forcing and maximum ratio transmission approaches. Through observations regarding the structure of the optimality conditions for each of the design formulations, low-complexity iterative algorithms that involve the evaluation of closed-form expressions are developed. In systems with a large number of antennas, the computational cost of some of these algorithms can be reduced to being linear in the number of antennas, without a significant degradation in performance. Simulation results show that the proposed robust designs can provide substantial reductions in the outage probability while satisfying the PAPCs.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".