Deep Reinforcement Learning-Enabled Resilient Radio Resource Allocation for Internet-of-Things Networks with Receiver Non-Linearity
Bibliographic record
Abstract
In recent years, the proliferation of internet of things (IoT) technologies has resulted in a significant increase in connected devices, leading to a growing demand for efficient resource allocation solutions to accommodate this rapid growth. However, a limitation of current IoT devices is that they contain non-linear analog components that cause signal distortions and adjacent channel interference (ACI). However, addressing the emerging resource allocation tasks using conventional methods is problematic, as these methods are associated with significant obstacles. Accordingly, extensive computational resources are needed to accurately estimate signal distortions caused by device non-linearity and ACI and to handle the optimization problem’s inherent non-convex nature. A promising technique that has recently emerged to address complex optimization challenges inherent in wireless networks is deep reinforcement learning (DRL). Using temporal information from the dynamic states of wireless networks, DRL is a robust approach for learning efficient resource allocation strategies. In this study, we propose a DRL-based resource allocation framework to maximize the sum throughput of an uplink orthogonal frequency-division multiple access (OFDMA)-based IoT network while accounting for both ACI and hardware impairments (HWIs). The proposed algorithm autonomously learns a policy, enabling the agent to adapt transmit power, modulation, and coding rate parameters based on the networks’ dynamic state information. Our simulation results reveal that the proposed scheme enhances the system’s throughput, particularly in scenarios with significant ACI and HWI-induced distortions, making it suitable for next-generation IoT networks.
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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 | 0.001 | 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 | 0.000 |
| 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".