Non-reciprocal RIS-Aided Full-Duplex Communications: IoT Applications
Bibliographic record
Abstract
The rapid growth of the Internet of Things (IoT) has increased the demand for efficient communication in scenarios where numerous devices sporadically transmit data to a base station (BS). This rise in wireless devices has led to spectrum congestion, driving the need for flexible and multifunctional full-duplex wireless systems. Reconfigurable Intelligent Surfaces (RIS) are particularly useful for enhancing data transmission in challenging environments, such as IoT devices in dead zones. RISs are programmable, ultra-compact, and can facilitate reciprocal and non-reciprocal signal transmissions in full-duplex mode, theoretically doubling the spectrum efficiency compared to half-duplex systems. Non-reciprocal RISs, with their independent transmission and reception paths, are ideal for full-duplex communication. This paper explores both reciprocal and non-reciprocal RIS configurations, deriving Signal-to-Interference-plus-Noise Ratio (SINR) metrics for both downlink and uplink scenarios. Optimal phase settings at the RIS are determined to maximize SINR, and simulation results demonstrate the advantages of RIS deployment in enhancing full-duplex communications for seamless connectivity in IoT systems. The study highlights the trade-offs between reciprocal and non-reciprocal RIS setups, with a focus on transmission power and RIS characteristics in system design. Additionally, the findings underscore the benefits of non-reciprocal RISs, particularly in leveraging directional transmission for improved system performance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".