SWIPT-Assisted Energy Efficiency Optimization in 5G/B5G Cooperative IoT Network
Bibliographic record
Abstract
Resource use in point-to-point and point-to-multipoint communication emerges with the tremendous growth in wireless communication technologies. One of the technologies is wireless power transfer which may be used to provide sufficient resources for energy-constrained networks. With the implication of cooperative communication in 5G/B5G and the Internet of Things (IoT), simultaneous wireless information and power transfer (SWIPT)-assisted energy efficiency and appropriate resource use become challenging tasks. In this paper, multiple IoT-enabled devices are deployed to cooperate with the source node through intermediate/relay nodes powered by radio-frequency (RF) energy. The relay forwards the desired information generated by the source node to the IoT devices with the fusion of decode/amplify processes and charges itself at the same time through energy harvesting technology. In this regard, a problem with throughput, energy efficiency, and joint throughput with user admission maximization is formulated while assuring the useful, practical network constraints, which contemplate the upper/lower bounds of power transmitted by the source node, channel condition, and energy harvesting. The formulated problem is a mixed-integer non-linear problem (MINLP). To solve the formulated problem, the rate of individual IoT-enabled devices (b/s), number of selected IoT devices, and the sum-rate maximization are prosecuted for no-cooperation, cooperation with diversity, and cooperation without diversity. Moreover, a comparison of the outer approximation algorithm (OAA) and mesh adaptive direct search algorithm (MADS) for non-linear optimization with the exhaustive search algorithm is provided. The results with reference to the complexity of the algorithms have also been evaluated which show that 4.68×10−10 OAA and 7.81×10−11 MADS as a percent of ESA, respectively. Numerous simulations are carried out to exhibit the usefulness of the analysis to achieve the convergence to ε-optimal solution.
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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 | 0.000 | 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 | 0.000 |
| 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".