Adaptive Resource Allocation for IoT With Computing Power Network Based on RIS-UAV-Aided NOMA-THz Communication
Bibliographic record
Abstract
The integration of advanced technologies such as sixth-generation mobile communications (6G), artificial intelligence (AI) and blockchain has given new impetus to the development of the Internet of Things (IoT). However, these applications require higher computational power and lower latency, which present challenges to traditional network architectures. To address these issues, this paper proposes a novel computing power network (CPN) architecture based on reconfigurable intelligent surface (RIS)-unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA)-Terahertz (THz) communication, and it aims to meet high computational demands. In the proposed scheme, CPN is introduced to assist IoT devices in executing tasks, thereby enhancing data processing. Concretely, THz communications and NOMA technologies are utilized to increase data rates and spectral efficiency. The combination of RIS and UAV shows promise in overcoming the challenges of high path loss and high sensitivity to blockage in THz communication, thereby improving system performance. To increase the efficiency of the proposed architecture, it is crucial to rationally allocate computational and transmission resources. Therefore, a joint optimization problem is formulated to minimize system consumption, encompassing both time and energy usage. To achieve efficient resource allocation, an adaptive N-Step method based on soft actor-critic (SAC) algorithm is employed. Simulation results demonstrate the superiority of the proposed method over the existing baselines
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".