Relay-Aided D2D MIMO Scheme (RAS) for Achieving Energy Efficiency in Satellite-Air-Ground Integrated Networks (SAGIN) Schéma D2D MIMO assisté par relais (RAS) pour atteindre l’efficacité énergétique dans les réseaux intégrés satellite-air-sol (SAGIN)
Bibliographic record
Abstract
Space-air-ground integrated network (SAGIN), as a three-tiered architecture that assimilates satellite systems, aerial, and terrestrial communication networks, has become an intensive research domain in the present era of communications. SAGIN-based communication models are developed to enhance the user’s quality of experience (QoE). Besides providing noteworthy benefits in various applications and services, SAGIN has unprecedented challenges because of its self-organized, unpredictable, and heterogeneous nature. Relaying equipment in SAGIN can be a very low-orbit satellite, a base station (BS), and an unmanned vehicle assisting a pair of mobile users’ communications. Thus, developing a robust device-to-device (D2D) direct and relaying communication model concerning channel distribution is crucial. Based on this concern, this article proposes a relay-aided D2D multiple–input and multiple–output (MIMO) scheme (RAS) for enhancing the optimal energy efficiency (EE) as a function of spectral efficiency (SE). The proposed model derives a relay-based amplify-and-forward (AF) MIMO multihop communication system for implementation. The proposed computations of optimal EE and SE for D2D MIMO show that the approximation provided by a random matrix approximation is constrained to a specific signal-to-noise ratio (SNR) range when the optimal SE and EE are derived using Gaussian quadrature and a hypergeometric function.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".