Active RIS-NOMA Uplink in URLLC, Jamming Mitigation via Surrogate and Deep Learning
Bibliographic record
Abstract
The integration of Non-Orthogonal Multiple Access (NOMA) and Reconfigurable Intelligent Surfaces (RIS) significantly enhances 5G across a variety of technologies such as the Internet of Things (IoT), smart cities, and industrial automation. This work explores an active RIS-assisted NOMA uplink system aimed at mitigating jamming attacks while ensuring the reliability and latency requirements of ultra-reliable low-latency communication (URLLC) applications. We investigate the potential of RIS with active elements that adjust the phase and amplitude of the received signals for robust jamming mitigation. The study incorporates finite blocklength (FBL) and Automatic Repeat Request (ARQ) strategies to handle real-world complex configurations effectively. A thorough examination of various network parameters is conducted, including user transmit powers, active RIS elements amplitude, and the number of RIS elements. The paper utilizes the surrogate optimization technique, particularly the Radial Basis Function (RBF), to address the non-convex optimization problem minimizing the power consumption. The complexity of the optimization problem, involving numerous interacting variables, leads us to develop a deep regression model to predict optimal network configurations, providing a computationally efficient approach as well as reducing the signaling overhead. The findings emphasize the delicate balance required in optimizing network parameters. For instance, increasing the blocklength from 100 to 150 increases the reliability feasibility by 12.19%. The results demonstrate an optimal range for the amplitude value of active RIS elements (2<β<15). Exceeding this range results in over-amplification, high latency, and lower reliability, due to the interference related to NOMA cluster users. The deep regression model converges to a weighted mean square error (WMSE) of 10.6 for RIS with 25 elements and 15.8 for larger RIS size, highlighting the effectiveness of the deep regression model and RIS configuration’s importance.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".