Robust Design for IRS-Assisted MISO-NOMA Systems: A DRL-Based Approach
Bibliographic record
Abstract
In this letter, we propose a robust design for an intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. In particular, the ergodic sum-rate maximization problem is formulated by taking into account the channel uncertainties of both direct links and the reflected links through IRS elements. The unbounded channel uncertainties with imperfect channel estimation are mathematically modelled based on the statistical channel state information (CSI) error model. However, the formulated ergodic sum-rate maximization problem with the outage-constraints is not jointly convex in terms of the beamforming vectors and the phase shifts of IRS elements, and hence it cannot be solved with the conventional optimization algorithms. To address the non-convexity issues and develop a joint design, the challenging robust design is reformulated as a reinforcement learning (RL) environment. Two deep RL agents are developed to jointly optimize the beamforming vectors and phase shifts of the IRS elements with the channel uncertainties and quality of service constraints. Simulation results are provided to validate the performance of the proposed agents for both fixed and dynamic channels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".