Joint Active and Passive Beamforming Design for IRS-Assisted Multi-User\n MIMO Systems: A VAMP-Based Approach
Bibliographic record
Abstract
This paper tackles the problem of joint active and passive beamforming\noptimization for an intelligent reflective surface (IRS)-assisted multi-user\ndownlink multiple-input multiple-output (MIMO) communication system. We aim to\nmaximize spectral efficiency of the users by minimizing the mean square error\n(MSE) of the received symbol. For this, a joint optimization problem is\nformulated under the minimum mean square error (MMSE) criterion. First, block\ncoordinate descent (BCD) is used to decouple the joint optimization into two\nsub-optimization problems to separately find the optimal active precoder at the\nbase station (BS) and the optimal matrix of phase shifters for the IRS. While\nthe MMSE active precoder is obtained in a closed form, the optimal phase\nshifters are found iteratively using a modified version (also introduced in\nthis paper) of the vector approximate message passing (VAMP) algorithm. We\nsolve the joint optimization problem for two different models for IRS phase\nshifts. First, we determine the optimal phase matrix under a unimodular\nconstraint on the reflection coefficients, and then under the constraint when\nthe IRS reflection coefficients are modeled by a reactive load, thereby\nvalidating the robustness of the proposed solution. Numerical results are\npresented to illustrate the performance of the proposed method using multiple\nchannel configurations. The results validate the superiority of the proposed\nsolution as it achieves higher throughput compared to state-of-the-art\ntechniques.\n
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".