Deep Learning-Based Channel Estimation and Dynamic IRS Assignment for Optimized Beamforming in IRS-Aided MC-NOMA Systems
Bibliographic record
Abstract
This paper proposes a novel framework for optimizing beamforming gain in multicarrier non-orthogonal multiple access (MC-NOMA) systems that are assisted by intelligent reflecting surfaces (IRS).The framework includes channel estimation based on deep learning, dynamic IRS assignment, and beamforming optimization.First, a convolutional neural network-long short-term memory (CNN-LSTM) model was implemented to estimate the channel state information (CSI) accurately.After that, a Qlearning agent utilizes this estimated CSI to allocate IRS elements to user clusters in a dynamic manner, with the objective of optimizing the use of IRS units according to the existing channel conditions.Using the allocated IRS elements and the estimated CSI, a deep Q-network (DQN) is developed to find the best beamforming vectors that save the most power and get the optimum signal-to-interference-plus-noise ratio (SINR).When compared to random IRS assignment and traditional heuristic beamforming optimization methods, the results show that the proposed framework shows significant improvements in SINR, power efficiency, and total system capacity.Simulation results show that the integration of deep learning and reinforcement learning techniques in the IRS-assisted MC-NOMA system can significantly improve the performance, indicating that the proposed framework is a potential solution for wireless communication in the future.
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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".