MIMO Device-to-Device Communications via Cooperative Dual-Polarized Intelligent Surfaces
Bibliographic record
Abstract
By making the propagation environment a programmable entity, reconfigurable intelligent surfaces (RIS) constitute a key enabler to fulfill the quality-of-service requirements of device-to-device (D2D) communications. In practice, however, a single RIS may not be able to establish a line-of-sight path between the devices, e.g., in environments with rich scattering or when devices are far apart. This can hinder the communications especially in applications that require the transfer of different data sets from a source to multiple destinations. Considering such type of applications, this letter proposes a dual-polarized cooperative RIS-assisted MIMO D2D communication scheme, where the beam routing path via multiple RISs is selected based on the maximum received signal-to-noise ratio and path constraints. Looking into system operation in indoor environments, and utilizing a realistic channel model, tractable expressions are obtained for the system’s error rate and outage probability. An asymptotic analysis is also conducted to evaluate the diversity gain. The findings reveal significant advantages of the dual-polarized cooperative RIS scheme over other RIS-based schemes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".