Spectrum Efficiency Maximization of Reconfigurable Intelligent Surface Assisted Device-to-Device Networks: An Actor-Critic Approach
Bibliographic record
Abstract
In recent years, Internet of Things (IoT) has become a radical evolution due to the exponentially growing demand for various promising applications toward the sixth-generation (6G) networks. As an energy-efficient and spectrum-efficient solution, device-to-device (D2D) communication has emerged as an enabling technology for 6G-based IoT networks. Recently, reconfigurable intelligent surface (RIS) has drawn much attention due to its capability to control the wireless environments so as to enhance the spectrum and energy efficiencies for the beyond fifth generation (B5G) wireless networks. Therefore, in this paper, a RIS-assisted D2D underlay cellular network is investigated to maximize the overall network’s spectrum efficiency (SE) by jointly optimizing the resource reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. Instead of using traditional optimization techniques to solve the formulated mixed integer problem, in this paper, a reinforcement learning (RL) based solution is utilized. The formulated optimization problem is modeled by the Markov Decision Process (MDP) in the RL environment. In order to learn the optimal policy under high-dimensional continuous-valued state and action spaces, an actor-critic algorithm based on the deep deterministic policy gradient (DDPG) scheme (AC-DDPG) is proposed. Simulation results reveal that the proposed AC-DDPG scheme achieves significant SE enhancements as compared to the state-of-the-art existing optimization schemes.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".