RIS-Assisted Space-Shift Keying With Non-Ideal Transceivers and Greedy Detection
Bibliographic record
Abstract
Reconfigurable intelligent surfaces (RIS) and index modulation (IM) represent key technologies for enabling reliable wireless communications with high energy efficiency. However, to fully take advantage of these technologies in practical deployments, comprehending the impact of the non-ideal nature of the underlying transceivers is paramount. In this vein, this paper introduces two RIS-assisted IM communication models, in which the RIS is part of the transmitter and space-shift keying (SSK) is employed for IM, and assesses their performance in the presence of hardware impairments. In the first model, the RIS acts as a passive reflector only, reflecting the incoming SSK modulated signal intelligently towards the desired receive diversity branch. The second model employs RIS as a modulator for sensing data, employing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>-ary phase-shift keying to implement reflection phase modulation (RPM) for such, and as a reflector for the incoming SSK modulated signal. Considering transmissions subjected to Nakagami-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading, and greedy detection at the reception, the performance of the RIS-assisted SSK and RIS-assisted SSK-RPM systems are evaluated in terms of the pairwise probability of erroneous index detection and the probability of erroneous index detection, for which closed-form expressions are obtained in both system configurations. Monte-Carlo simulations are carried out to validate the analytical framework, and numerical results are presented to study the dependency of the error performance on the main system parameters. The findings highlight the effect of hardware impairments on the system performance. In particular, minimal to no impact is observed in the low ranges of average signal-to-noise ratio (SNR), while the effect is prominent for higher average SNRs. The study emphasizes using such greedy detectors, which are robust to the said impairments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".