A Novel Information-Directed Tree-Search Algorithm for RIS Phase Optimization in Massive MIMO
Bibliographic record
Abstract
In this paper, we propose a novel Information-Directed Branch-and-Prune (IDBP) algorithm to optimize the discrete phase settings of a passive Reconfigurable Intelligent Surfaces (RIS) to improve the blocked line-of-sight (LOS) link performance in the Massive Multiple-Input Multiple-Output (MaMIMO) wireless networks. The MaMIMO transceivers are assumed to be equipped with a hybrid precoder, combiner, and low-resolution analog-to-digital converters (ADCs). Mean Squared Error (MSE) of the received and combined symbol at the receiver is used as the performance metric. We derive the expression for the Cramer-Rao lower bound (CRLB) of the MSE as a function of the RIS phase shift settings for a given hybrid precoder, combiner, and ADC bits. The proposed IDBP algorithm arrives at the RIS phase settings that minimize the CRLB using an information-theoretic measure that define the pruning rules in the tree traversal. Using the proposed algorithm and with an appropriate design of the hybrid precoder and combiner, it can be shown that the MSE achieves the CRLB. We claim theoretical guarantees for near-optimal MSE performance and the throughput for a blocked LOS link with reduced 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".