IRS-Enabled Monostatic Backscatter MIMO Communication Design for V2I Networks
Bibliographic record
Abstract
Future vehicle-to-infrastructure (V2I) wireless networks are meeting unprecedented challenges regarding the high energy consumption problem caused by the large number of radio frequency (RF) emitters deployed. The backscatter communication (BackCom) and Intelligent Reflecting Surface (IRS) techniques can achieve low-power transmission while implementing effective beamforming. Thus, this paper leverages these two techniques into V2I communication systems, where multiple IRSs transmit roadside information to a multi-antenna vehicle by backscattering the vehicle's signals. Considering the self-interference and two optimization methods at the vehicle, i.e., post-detection (PD) and integrated detection (ID), we formulate two weighted sum-rate maximization problems. The detection matrix and transmit power at the vehicle, as well as the IRS reflection coefficients, are jointly designed based on an alternating algorithm. The simulation results validate the convergence performance of the proposed algorithm, the feasibility of the introduced system model, and the effectiveness of the optimization scheme.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".