Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels
Bibliographic record
Abstract
This article presents a reinforcement learning-based approach to improve the physical layer security of an underlay cognitive radio network over cascaded channels. These channels are utilized in highly mobile networks such as cognitive vehicular networks (CVN). In addition, an eavesdropper aims to intercept the communications between secondary users (SUs). The SU receiver has full-duplex and energy harvesting capabilities to generate jamming signals to confound the eavesdropper and enhance security. Moreover, the SU transmitter extracts energy from ambient radio frequency signals in order to power subsequent transmissions to its intended receiver. To optimize the privacy and reliability of the SUs in a CVN, a deep Q-network (DQN) strategy is utilized where multiple DQN agents are required such that an agent is assigned at each SU transmitter. The objective for the SUs is to determine the optimal transmission power and decide whether to collect energy or transmit messages during each time period in order to maximize their secrecy rate. Thereafter, we propose a DQN approach to maximize the throughput of the SUs while respecting the interference threshold acceptable at the receiver of the primary user. According to our findings, our strategy outperforms two other baseline strategies in terms of security and reliability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".