Impact of Finite-Resolution Precoding and Limited Feedback on Rates of IRS Based mmWave Networks
Bibliographic record
Abstract
Intelligent reflecting surfaces (IRSs) can enhance the system performance of millimeter wave (mmWave) networks. In practice, phase shifts at the base station and the IRSs are digitally controlled and have discrete values. The channel state information (CSI) feedback also has a limited rate. This work analyzes the spectral efficiency performance of an IRS based mmWave network in which the beamsteering codebooks are used for the passive precoding and analog precoding, and the zero-forcing precoder is used for digital precoding. To capture the features of mmWave channels and reflect the impact of passive and analog precoding, a channel correlation based codebook is employed to quantize the effective channels. Upper-bounds are derived for the spectral efficiency losses due to finite-resolution beamsteering codebooks and limited CSI feedback. It is found that the spectral efficiency loss due to finite-resolution beamsteering codebooks does not depend on the transmit power in large signal-to-noise ratio regimes, while the spectral efficiency loss due to limited CSI feedback increases with the transmit power. Then, the asymptotic performance is analyzed when the numbers of transmit antenna elements and reflecting elements go to infinity. Simulations verify the derivations and findings.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".