RIS Narrow Beamwidth and Link Selection for Improving Connectivity of Multi-RIS-Assisted D2D Networks
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) has been proposed to add some level of reconfigurability to the propagation medium via deploying a surface that consists of digitally-controllable reflecting elements. To fully demonstrate the effectiveness of integrating RIS technology with device-to-device (D2D) communications, this paper designs RIS narrow beamwidth, enabling the creation of multiple cascaded links, called RIS-aided links, to connect blocked user equipment (UE). Specifically, this work designs narrow beamwidth RISs to enhance the connectivity of multi-RIS-assisted D2D networks through a unique phase shift determination. The proposed design optimizes the power-domain array factor (PDAF), aiming to target specific azimuth angles of reliable UEs and improve network connectivity. We formulate the network connectivity optimization problem that jointly optimizes RIS narrow beamwidth design and RIS-aided link selection. This problem is a mixed integer non-linear program (MINLP), thus we tackle it by proposing an effective approach, referred to as continuous genetic algorithm (CGA)-RIS. First, we analyze and design the RIS narrow beamwidth using CGA, where azimuth angles of receiving UEs are not precisely known. For this optimization task, we aim at generating multiple RIS-aided links that exhibits significant PDAF towards reliable UEs while minimizing PDAF towards unreliable UEs. The optimization problem of RIS-aided link selection is then solved using an efficient perturbation method while employing the designed CGA for RIS narrow beamwidth. The numerical results demonstrate that a significant performance improvement can be achieved by the proposed approach. Specifically, compared to existing network connectivity schemes, our proposed approach shows superior performance compared to other scenarios, including distributed small RISs and traditional D2D.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".