Intelligent Reflecting Surface Enabled Random Rotations Scheme for the\n MISO Broadcast Channel
Bibliographic record
Abstract
The current literature on intelligent reflecting surface (IRS) focuses on\noptimizing the IRS phase shifts to yield coherent beamforming gains, under the\nassumption of perfect channel state information (CSI) of individual\nIRS-assisted links, which is highly impractical. This work, instead, considers\nthe random rotations scheme at the IRS in which the reflecting elements only\nemploy random phase rotations without requiring any CSI. The only CSI then\nneeded is at the base station (BS) of the overall channel to implement the\nbeamforming transmission scheme. Under this framework, we derive the sum-rate\nscaling laws in the large number of users regime for the IRS-assisted\nmultiple-input single-output (MISO) broadcast channel, with optimal dirty paper\ncoding (DPC) scheme and the lower-complexity random beamforming (RBF) and\ndeterministic beamforming (DBF) schemes at the BS. The random rotations scheme\nincreases the sum-rate by exploiting multi-user diversity, but also compromises\nthe gain to some extent due to correlation. Finally, energy efficiency\nmaximization problems in terms of the number of BS antennas, IRS elements and\ntransmit power are solved using the derived scaling laws. Simulation results\nshow the proposed scheme to improve the sum-rate, with performance becoming\nclose to that under coherent beamforming for a large number of users.\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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".