On Resource Allocation of Cooperative Multiple Access Strategy in Energy-Efficient Industrial Internet of Things
Bibliographic record
Abstract
In this article, we investigate the jointly optimized resource allocation with hybrid multiple access in energy-efficient industrial Internet of Things (IIoT), where some devices (e.g., those for critical control devices) have higher transmission priority and stable energy supply while some devices (e.g., those for comprehensive sensors) may not. We consider a system model supporting wireless powered IIoT devices, with certain user terminal as a potential relay for the transmission between a hybrid access point and another user terminal. Constrained by the limited energy storage, the user needs to harvest energy before relaying and only the harvested energy is utilized for the following transmission. We propose a collaborative orthogonal and nonorthogonal multiple access protocol where two cooperation schemes with and without decoding the relay message are applied. Jointly considering time sharing in the transmission process, power splitting for simultaneous wireless information and power transfer, and transmit power allocation at the cooperative user, the achievable rate regions under the Rayleigh fading channel model are derived. Based on which, an optimization problem on resource allocation strategies is formulated and discussed. Both analytical and numerical results are provided, illustrating the impact of user geometry on the achievable rates as well as the optimal resource allocation with different cooperative strategies applied in different use cases. Aiming to enhance resource utilization, energy-efficient cooperation enables the combination of various transmission modes and networking classes in large scale networks, as well as a better use of ambient radio frequency signals for wireless powered transmissions.
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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 | 0.001 |
| 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 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".