SWIPT and uplink NOMA approach for self energy recycling in full-duplex enabled D2D network
Why this work is in the frame
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Bibliographic record
Abstract
Simultaneous wireless information and power transfer (SWIPT) is a method through which users can simultaneously obtain energy and receive data from the base station (BS). This allows them to charge their batteries, which have limited power capacity, and enhance the quality of service (QoS). Nevertheless, this technology encounters the double near-far issue, where users located at the edges of the cell experience insufficient energy due to energy path loss, resulting in a decrease in system performance. In this investigation, we integrate the self-energy recycling technology with a D2D communication system that enables full-duplex operation. By employing this technology, the D2D cell edge user (DCEU) can harvest energy from self-interference and utilize it to transmit data to the nearest D2D cell center user (DCCU). The DCCU combines its data with the DCEU and sends it to the BS using uplink non-orthogonal multiple access (NOMA). Both the DCCU and DCEU use a significant amount of energy when transmitting data, which negatively affects the quality of service (QoS). The goal of this research is to decrease the overall energy consumption of the DCEU and DCCU while maintaining their QoS. The defined problem is complex due to time and power limitations, as well as the presence of self and NOMA interference. To address this issue, we utilize a technique that combines time and power allocation. The numerical results demonstrated that the proposed scheme consumes 28.12% and 40.67% lesser amount of energy as compared to the baseline schemes.
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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 | 0.000 |
| 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