Intelligent Reflecting Surface-Assisted mmWave Communication Exploiting Statistical CSI
Bibliographic record
Abstract
Intelligent reflecting surface (IRS) is a new technique to improve the ergodic capacity in wireless networks. IRS consists of a large number of passive elements which digitally manipulate electromagnetic waves, and thus can act as a passive precoder in the communication. This paper introduces the IRS to millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems. Specifically, we consider a joint design of hybrid precoders at the base station (BS) and the passive precoder at the IRS to maximize the ergodic capacity of the system. In particular, since the instantaneous channel state information (CSI) of the BS-IRS link and the IRS-user link is challenging to obtain in practice, the statistical CSI is exploited for the joint hybrid and passive precoder design. However, such a design problem is challenging to solve due to the non-convexity. Thus, the block-coordinate-descent based algorithms are proposed to solve the problem efficiently. Simulation results demonstrate that, compared with the traditional systems without IRSs, the joint design of hybrid and passive precoding improves the ergodic capacity significantly. The results also show that adding some low-cost reflector elements at the IRS can help reduce the number of high-cost RF chains in the BS of the IRS-assisted mmWave MIMO systems.
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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) | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".