Vision-Assisted User Clustering for Robust mmWave-NOMA Systems
Bibliographic record
Abstract
When operated in the mmWave band, user channels get highly correlated which can be exploited in mmWave-NOMA systems to cluster a set of “correlated” users together. Identifying the set of users to cluster greatly affects the viability of NOMA systems. Typically, only channel state information (CSI) is used to make these clustering decisions. When any problem arises in accessing up-to-date and accurate CSI, user clustering will not properly function due to its hard-dependency on CSI, and obviously, this will negatively affect the robustness of the NOMA systems. To improve the robustness of the NOMA systems, we propose to utilize emerging trends such as location-aware and camera-equipped base stations (CBSs) which do not require any extra radio frequency resource consumption. Specifically, we explore three different dimensions of feedback that a CBS can benefit from to solve the user clustering problem, namely CSI-based feedback and non-CSI-based feedback, comprised of user equipment (UE) location and the CBS camera feed. We first investigate how the vision assistance of a CBS can be used in conjunction with other dimensions of feedback to make clustering decisions in various scenarios. Later, we provide a simple user case study to illustrate how to implement vision-assisted user clustering in mmWave-NOMA systems to improve robustness, in which a deep learning (DL) beam selection algorithm is trained on the images captured by the CBS to perform NOMA clustering. We demonstrate that user clustering without CSI can achieve comparable performance to accurate CSI-based solutions, and user clustering can continue to function without much performance loss even in the scenarios where CSI is severely outdated or not available at all.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".