Energy-Efficient Clustering and Power Allocation in RSMA-Enabled IoT Networks with Finite Blocklength Coding and Hardware Impairments
Bibliographic record
Abstract
As the number of internet of things (IoT) devices grows rapidly, ensuring energy efficiency (EE) in IoT networks is becoming a crucial concern. In dense IoT networks, scheduling multiple IoT devices over the same radio resource blocks (RRBs) simultaneously causes interference that can severely degrade a network’s EE. This paper investigates a resource optimization problem for IoT networks that considers both finite blocklength (FBL)-coded transmission and signal distortions caused by practical low-complexity radio frequency front ends that lead to hardware impairments (HWIs). We exploit ratesplitting multiple access (RSMA) to schedule multiple IoT devices over the same RRB. We propose to jointly optimize device clustering and transmit power allocation in order to enhance the network’s EE while managing interference. A twostep framework is devised to solve the optimization problem in a distributed and computationally efficient manner. First, IoT devices are clustered into non-overlapping groups and the RSMA strategy is implemented in each cluster. Second, a deep-Q network-based multi-agent reinforcement learning framework is employed to near-optimally allocate transmit power among the devices in each cluster. Our simulation results shown that our proposed framework enhances the EE of a downlink RSMA IoT network notably more than state-of-the-art transmit PA schemes do in the presence of FBL coding and HWI-induced distortions.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".