Zero SolarWing: A Net-Zero Solar Wind-Powered UAV-Enabled RIS System for URLLC Services in 6G Compute First Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The transition to sixth-generation (6G) networks demands innovative solutions to address the challenges of energy efficiency, ultra-reliable low-latency communications (URLLC), and sustainable network architectures. Recently, Compute First Networking (CFN) has emerged as a transformative paradigm, enabling efficient integration of computation and communication while addressing critical issues such as energy efficiency and system reliability. In response to these imperatives, the integration of net-zero solar wind-powered unmanned aerial vehicle (UAV)-assisted reconfigurable intelligent surface (RIS) with CFN systems emerges as a pivotal solution for enabling URLLC services. This integration not only meets stringent computation requirements but also minimizes environmental impact, paving the way for sustainable and reliable next-generation networks. In addressing this challenge, our proposed solution, named Zero SolarWing, harnesses renewable energy sources, specifically solar and wind power, to sustainably power UAV coupled with RIS technology. This innovative integration not only reduces carbon emissions but also enhances ultra-high reliability. Our approach includes the formulation of a minimization problem aimed at mitigating total decoding error subject to blocklength allocation and UAV positioning. Through comprehensive simulation studies, we demonstrate the convergence and superior performance of our proposed method compared to fixed benchmarks. Lastly, we show feasibility of our approach in achieving a net-zero system where harvested and consumed energies are equivalent as well as attaining optimal UAV positioning to minimize total decoding error.
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.
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 | 0.000 | 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 it