Securing Cognitive IoT Networks: Reinforcement Learning for Adaptive Physical Layer Defense
Bibliographic record
Abstract
Single-input multiple-output (SIMO) configurations are commonly employed in applications where managing power consumption is critical. This is the case for battery-powered sensors and low-power Internet of Things (IoT) devices, which may also employ energy harvesting (EH) strategies to augment their battery longevity. These systems can further access the underutilized spectrum through cognitive radio networks (CRNs). This study focuses on mitigating eavesdropping concerns in such configurations through the application of physical layer security (PLS). More specifically, the paper proposes a PLS technique to evaluate and enhance the confidentiality of secondary users' (SUs) transmissions for SIMO underlay CRN, considering the presence of an eavesdropper. The secondary user receiver incorporates the power splitting-EH method to extract energy, subsequently utilizing it to generate jamming signals to perplex the eavesdropper. We compare the results obtained when the eavesdropper extracts energy from the SUs' transmissions and when it chooses to only decode the wiretapped messages. We implement a deep reinforcement learning (DRL) approach [1], specifically the deep Q-network, to optimize the transmission power and thereby maximize the secrecy rate of the SUs and demonstrate that the performance of the approach surpasses that of the benchmarks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".