Rate Adaptation and Power Control for IoT Networks With Ambient Energy Harvesting: A Deep Reinforcement Learning Approach
Bibliographic record
Abstract
In Internet of Things (IoT) networks, ensuring the timely delivery of information is significantly constrained by the limited energy resources of IoT devices and the signal attenuation experienced in wireless channels. In this paper, we investigate resource management for self-sustaining IoT networks with ambient radio frequency (RF) energy harvesting via a spatio-temporal approach. We consider a hard deadline for packet delivery, and we aim to jointly reduce the age of information (AoI) and the packet drop rate due to the hard deadline for packet delivery or buffer overflow. To achieve that, using tools from deep reinforcement learning (DRL) and stochastic geometry, we propose a joint rate adaptation and power control scheme that accounts for the spatial topology of the network and the temporal attributes at the device level. In particular, stochastic geometry is leveraged to characterize the energy harvesting process and the packet transmission success probability for a given transmit rate and power. Furthermore, the joint rate adaptation and power control policy at the device level is obtained using a deep R-network (DRN), which is a DRL algorithm that utilizes a deep neural network to approximate the R-function (the expected average reward). The performances of the last-come-first-served (LCFS) queuing discipline, the first-come-first-served (FCFS) queuing discipline, and a proposed hybrid queuing discipline are compared. For the proposed hybrid queuing discipline, DRL is used not only for rate adaptation and power control but also for specifying the transmission order of generated packets. The presented numerical results demonstrate that the LCFS queuing discipline improves AoI performance, while the FCFS queuing discipline improves packet drop rate. Also, the proposed hybrid queuing discipline strikes an intricate balance between AoI and packet drop rate, and achieves a good performance in both measures compared to the other queuing disciplines.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".