EMF-reduction Uplink Resource Allocation Scheme for Non-Orthogonal Multiple Access Systems
Bibliographic record
Abstract
The increasing number of users and the demand for higher data rate due to multimedia services, requires high capacity/bandwidth, low latency and high quality of service (QoS). In turn, some of these requirements have/will lead to the deployment of numerous new access points. Given that wireless communication systems utilize radio frequency (RF) waves to operate, more users and more access points imply more electromagnetic field (EMF) exposure. Meanwhile, the possible health consequences of EMF exposure from these systems are progressively becoming a major concern due to their ever growing ubiquity and increased transmission power. In an endeavor to reduce the EMF exposure due to wireless communication systems, we design an optimization scheme for minimizing the EMF exposure of users in the uplink of non-orthogonal multiple access (NOMA) systems, while satisfying each user with an acceptable QoS. We define EMF minimization strategy as a convex optimization problem and iteratively solve it by assigning bits to each user. Each subcarrier is strategically allocated to a group of users having minimum interference and is found by using the classic binary search algorithm. Simulation results depict that our proposed strategy provides a reduction of at least 1 order of magnitude in terms of EMF exposure, while satisfying QoS constraints, in comparison with the state-of-art techniques in the literature.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".