Multi-way communication using reflecting intelligent surfaces: Decoding algorithms and error rate performance
Bibliographic record
Abstract
In this work, we present a reconfigurable intelligent surface (RIS) aided multi-way communication systems employing QPSK.The objectives of the system is to allow all users exchange information, exploiting the full-duplex capabilities of RIS.The system under consideration comprises K user terminals with N RIS reflective elements.Each user terminal is equipped with one transmit antenna and M receive antennas.To mitigate inter-user interference caused by the full-duplex operation of the RIS, three successive interference cancellation (SIC) techniques are considered for this system.We simulate the bit error rate (BER) for three SIC ordering methods: 1) descending channel variances, 2) increasing mean square error, 3) an optimization approach which maximizes the combined channel gain within the system.The BER performances of these three SIC methods are compared for different numbers of RIS elements, ranging from 4 to 128, and a varying number of transmitting users, ranging from 1 to 5. Additionally, a varying RIS phase shift model with non-uniform amplitude is taken into consideration.We adapt the previous SIC optimization problem to incorporate a phase-dependent amplitude expression.Simulation results for probability of error indicate that in both fixed and unfixed scenarios, when the number of RIS elements is small, the second SIC ordering technique achieves the best BER performance.However, as the number of RIS elements increases, the third SIC ordering technique achieves the best BER performance.ii Sommaire Dans ce travail, nous présentons un système de communication multi-voies assisté par une surface intelligente reconfigurable (RIS) utilisant la modulation QPSK.Les objectifs du système sont de permettre à tous les utilisateurs d'échanger des informations, en exploitant les capacités de duplex intégral des RIS.Le système considéré comprend K terminaux d'utilisateurs avec N éléments réfléchissants.Chaque terminal utilisateur est équipé d'une antenne de transmission et de M antennes de réception.Pour atténuer les interférences inter-utilisateurs causées par le fonctionnement complet du RIS, trois techniques d'annulation d'interférence successive (SIC) sont prises en compte pour ce système. Nous simulons le taux d'erreur (BER) pour trois méthodes d'ordonnancement SIC : 1) variances de canal descendantes, 2) erreur quadratique moyenne croissante et 3) une approche d'optimisation qui maximise le gain de canal combiné au sein du système.Les performances BER de ces trois méthodes SIC sont comparées pour différents nombres d'éléments RIS, allant de quatre à 128, et un nombre variable d'utilisateurs transmettant, allant de un à cinq.De plus, un modèle de déphasage RIS variable avec une amplitude non uniforme est pris en compte.Nous adaptons le problème d'optimisation du SIC précédent pour incorporer une expression d'amplitude dépendante de la phase.Les résultats de simulation pour la probabilité d'erreur indiquent que dans les scénarios fixes et non fixes, lorsque le nombre d'éléments RIS est petit, la deuxième technique d'ordonnancement SIC atteint la meilleure performance BER.Cependant, à mesure que le nombre d'éléments RIS augmente, la troisième méthode d'ordonnancement SIC réalise la meilleure performance BER.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".