Analysis of Intelligent Reflecting Surface-Assisted mmWave Doubly Massive-MIMO Communications
Bibliographic record
Abstract
The emerging novel intelligent reflecting surface (IRS) is envisioned to be an important technique for future wireless networks in terms of enhancing both spectrum efficiency and energy efficiency. This paper is concerned with a millimeter-wave (mmWave) single-user system aided by an IRS which consists of several subsurfaces, each having the same number of passive reflecting elements. The achievable sum rate of such an IRS-aided system is derived under the assumption that both transmit and receive terminals are equipped with very large antenna arrays. Furthermore, with the objective of maximizing the sum rate, optimal solutions of precoding/combining, IRS's phase shifts, and power allocation are presented. Then it is shown that the multiplexing gain of the IRS-aided system increases with the number of subsurfaces while the power gain increases quadratically as the number of reflecting elements at each subsurface increases. Finally, numerical results are presented to corroborate analytical results.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".