RSMA-Enabled Interference Management for Industrial Internet of Things Networks With Finite Blocklength Coding and Hardware Impairments
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Bibliographic record
Abstract
The increasing proliferation of industrial internet of things (IIoT) devices requires the development of efficient radio resource allocation techniques to optimize spectrum utilization. In densely populated IIoT networks, the interference that results from simultaneously scheduling multiple IIoT devices over the same radio resource blocks (RRBs) severely degrades a network’s achievable capacity. This paper investigates an interference management problem for IIoT networks that considers both finite blocklength (FBL)-coded transmission and signal distortions induced by hardware impairments (HWIs) arising from practical, low-complexity radio-frequency front ends. We use the rate-splitting multiple access (RSMA) scheme to effectively schedule multiple IIoT devices in a cluster over the same RRB(s). To enhance the system’s achievable capacity, a joint clustering and transmit power allocation (PA) problem is formulated. To tackle the optimization problem’s inherent computational intractability due to its non-convex structure, a two-step distributed clustering and power management (DCPM) framework is proposed. First, the DCPM framework obtains a set of clustered devices for each access point by employing a greedy clustering algorithm while maximizing the clustered devices’ signal-to-interference-plus-noise ratio. Then, the DCPM framework employs a multi-agent deep reinforcement learning (DRL) framework to optimize transmit PA among the clustered devices. The proposed DRL algorithm learns a suitable transmit PA policy that does not require precise information about instantaneous signal distortions. Our simulation results demonstrate that our proposed DCPM framework adapts seamlessly to varying channel conditions and outperforms several benchmark schemes with and without HWI-induced signal distortions.
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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 | 0.000 |
| 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 it