Device-to-Device Communications With Selection-Based Cooperative RIS
Bibliographic record
Abstract
This work amalgamates spatial modulation (SM) with ambient backscattering (ABSc) to address the spectral and energy efficiency demands of the power constrained device-to-device (D2D) communications in the Internet-of-things. Though incorporating reconfigurable intelligent surfaces (RISs) in the communication process can help in extending the coverage of such power constrained devices, rich scattering in the operation environment, or broken links between the nodes involved in the end-to-end communication, can adversely affect the system performance. To cope up with this challenge, a selection-based cooperative RIS protocol is proposed, and the performance of the D2D communication system, founded on SM and ABSc at the transmitter and cooperative RISs, is evaluated in terms of the bit error rate, outage probability, and energy efficiency. A link budget analysis is conducted to comprehend the effects of the RIS sizes in countering the path loss effects, and the imperfection of the channel estimation and timing synchronization of multiple RISs are also analyzed. The results reveal that the proposed communication model with cooperative RISs can overcome the path loss effects and enhance the received power levels, thereby outperforming the baseline system where a single RIS intervenes in the end-to-end communication, with a signal-to-noise ratio gain of around 10 dB for the bit error rate, outage probability, and energy efficiency. Considering different prominent SM techniques for the system operation and comparing the performance in different set-ups, it is shown that the system implementing generalized SM performs the best.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".