RIS Empowered Index Modulation-Based Receive Diversity Wireless System With Nakagami-$m$ Fading Channels
Bibliographic record
Abstract
Reconfigurable intelligent surfaces (RIS) and index modulation (IM) have been proven as potential technologies for improving the performance of next-generation wireless communication systems. In this paper, we consider the study of a receive diversity RIS-assisted wireless communication system employing two IM schemes, namely, space-shift keying (SSK) and spatial modulation (SM) for data transmission over Nakagami-$m$fading channels. Considering the RIS to lie in close proximity to the transmitter, a receiver structure based on a greedy detection rule is proposed to select one of the receive diversity branches with the highest received energy for demodulation. Based on this system model, novel closed-form expressions for the probability of erroneous index detection (PED) of the considered target receive diversity branch, and the corresponding asymptotic expressions at a high signal-to-noise ratio (SNR) are obtained using a characteristic function approach. Furthermore, closed-form and asymptotic expressions at high SNR for the bit error rate (BER) for the SSK-based system and the SM-based system employing$M$-ary phase-shift keying and$M$-ary quadrature amplitude modulation schemes are also derived. The dependencies of the performance of the considered system are also corroborated via numerical results. The asymptotic expressions and results of PED and BER at high and low SNR values lead to the observation of a performance saturation and the presence of an SNR value as a point of inflection attributed to the greedy detector's structure.
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) | 0.001 | 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 | 0.001 | 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".