Polarization-Enabled MIMO Bidirectional Device-to-Device Communications via RIS
Bibliographic record
Abstract
In future wireless networks, device-to-device (D2D) communications are expected to play an important role to support a plethora of applications. To meet the target quality of service while ensuring high reliability, and high spectral and energy efficiencies, manipulation of the radio waves by reconfigurable intelligent surfaces (RIS) will be critical. In this context, leveraging concepts of polarization, this paper proposes a framework for bidirectional D2D communications, where the data exchange between a central node and devices operating in distinct polarization states, is multiple-input multiple-output in nature and takes place via a dual-polarized RIS, in the presence of hardware impairments and imperfect interference cancellation, as well as impairments caused by the spatial correlation and cross-polarization. Performance evaluation of such a framework is conducted in terms of key metrics, namely, bit error rate, outage probability, channel capacity, and energy efficiency, for which closed-form expressions are obtained considering transmissions over Nakagami fading channels. An asymptotic analysis is also conducted to evaluate the achievable diversity gains, by approximating the Nakagami model with a tractable Gamma model. Further, the impact of imperfect channel estimation on performance is also investigated. Comparative numerical results are provided, and the effects of the main system parameters on performance are analyzed. The proposed framework is shown to provide significant performance improvements as compared to D2D communications via non-polarized RIS.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".