Deep Reinforcement Learning for RSMA-Based Multi-Functional Wireless Networks
Bibliographic record
Abstract
The upcoming sixth generation (6G) is expected to support a wide range of applications that require efficient sensing, accurate localization, and reliable communication capabilities. Furthermore, 6G is expected to catalyze the development of new use cases that will require working in extreme environmental and hazardous conditions and have ultra-small size and low-cost wireless devices. Thus, developing sustainable multi-functional wireless networks that are capable of incorporating billions of low-power devices and supporting their sensing and communication requirements on top of energy harvesting capability is of paramount importance. Motivated by this, we consider in this work a rate-splitting multiple access (RSMA)-based multifunctional wireless network with sensing, energy harvesting, and communication capabilities. We employ trust region policy optimization (TRPO), a deep reinforcement learning (DRL) algorithm, to efficiently allocate the available resources and manage the interference between the three functionalities. TRPO/DRL is capable to learn a near-optimal policy for the resource allocation problem in a complex and dynamic environment. This enables us to obtain near-optimal transmit precoders, power splitting ratios, and ratesplitting among the common and private rates in a multiple access setting. Simulation results demonstrate the effectiveness of RSMA in mitigating the interference in such multi-functional networks and its capability to accommodate the rate and energy harvesting requirements of the devices while still capable of sensing multiple targets.
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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 | 0.003 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".