Finite Blocklength Relaying Communication With Unitary Beamforming and Energy Harvesting: Fairness Oriented Design
Bibliographic record
Abstract
Energy-efficient wireless communications are very important for the future Internet of Things (IoT). In this article, a finite blocklength relaying network with nonlinear energy harvesting for IoT communications is proposed. A base station (BS) is considered that transfers data and energy to a local node that harvests energy. This node further employs the amplify and forward protocol for relaying information to a remote node. Our goal is to maximize the energy efficiency of the BS from the perspective of fairness. A multiagent deep reinforcement learning algorithm is proposed to arrive at near-optimal transmission and precoding parameters in real time. The case where global channel state information (CSI) for the BS and local node is considered as well as when only partial CSI is available. Numerical results are presented to illustrate the design tradeoffs and verify the performance of the proposed approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".