Energy-Efficient Resource Allocation in Cellular Network with Ambient RF Energy Harvesting
Bibliographic record
Abstract
Simultaneous wireless information and power transfer is an innovative way to provide electrical energy for user equipments. However, most previous works mainly focus on energy harvesting over a relatively narrow frequency range. Due to small energy harvested by the users, the practical applications are usually limited to low power devices. In this paper, an energy-efficient uplink resource allocation problem is investigated in a cellular network with ambient radio frequency (RF) energy harvesting. In order to obtain sufficient energy, a broadband rectenna is adopted to harvest ambient RF energy over six frequency bands at the same time. From the viewpoint of service arrival in the ambient transmitter, a new energy arrival model is presented. The problem of sub-carrier and power allocation is formulated as a mixed-integer nonlinear programming problem. The objective is to maximize the energy efficiency while satisfying the energy causality and the total data rate requirement. In order to reduce the computational complexity, a suboptimal solution is derived by employing a quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the QPSO method has higher energy efficiency than a conventional particle swarm optimization approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".