Joint Transmission Mode Selection and Scheduling for AoI Minimization in NOMA-Capable WP-IoT Networks: A Deep Transfer Learning Solution
Bibliographic record
Abstract
Age of information (AoI) serves as a key metric for characterizing information freshness. In this article, we investigate the AoI minimization of a non-orthogonal multiple access (NOMA)-capable wireless-powered Internet of Things (WP-IoT) network, where a base station (BS) consistently sends radio frequency (RF) signals to power IoT sensors (IoT-Ss), and selected IoT-Ss are scheduled to transmit status update packets to the BS in each time slot. We first formulate a scheduling problem for average AoI (AAoI) minimization with NOMA transmission and solve for a near-optimal scheduling policy with a deep Q-network (DQN)-based solution. Next, we propose a novel joint transmission mode selection and scheduling (JTMSS) design to further minimize the AAoI of the network. Specifically, the system adaptively selects one of three transmission modes: NOMA, orthogonal multiple access (OMA), and no transmission and schedules two, one, or none sensors for transmission, respectively. Considering the discrete hierarchical action space of the JTMSS problem, we formulate a parameterized-action Markov Decision Process (PAMDP) and develop a deep transfer learning (DTL)-based solution with a two-tier DQN framework to find a near-optimal JTMSS policy. Besides, we present a partial tuning approach during online operation to alleviate the effects of environmental changes. Simulation results verify that the JTMSS design with DTL achieves a significant performance gain over the scheduling-only policy for NOMA transmission. Moreover, the online tuning with DTL converges quickly during online operation.
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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 | 0.001 | 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".