An Enhanced Interference Alignment Strategy With MIL Criterion and RCG Algorithm for IRS-Assisted Multiuser MIMO
Bibliographic record
Abstract
An enhanced interference alignment strategy with minimum interference leakage (MIL) criterion and Riemannian conjugate gradient (RCG) algorithm is proposed for intelligent reflecting surface (IRS)-assisted multiuser multiple-input multiple-output (MIMO). In this letter, the maximum sum rate is formulated as the optimization objective, with alternate optimization of phase shift vector at IRS as well as precoding and interference suppression vectors at transceivers respectively. MIL interference alignment criterion is used to iteratively solve the precoding vector and the interference suppression vector via channel reciprocity property. RCG algorithm is further applied to derive the IRS phase shift vector and maximize sum rate with the condition of ensuring a given minimum interference leakage threshold to eliminate system interference. Simulation results reveal that the proposed strategy effectively enhances sum rate performance compared with the scheme using random IRS phase shift vector and random precoding/interference suppression vectors as well as “AP + RCG” scheme in IRS-assisted multiuser single-input single-output (SISO) scenario. In addition, compared with “MMSE + RCG” strategy in the case of same multi-antenna transceiver pairs and antenna numbers, the proposed strategy could make a tradeoff between sum rate performance and computational complexity.
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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 | 0.001 |
| 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".