Efficient Radio Wave Propagation Prediction Using Dynamic GAN-Based Model with Data Augmentation
Bibliographic record
Abstract
Accurate modeling of radio wave propagation in indoor environments is essential for optimizing modern wireless communication systems. Traditional deterministic methods, such as ray-tracing or full-wave simulations, offer precise solutions but are computationally expensive, especially when high spatial resolution over large areas is required. This paper presents a dynamic, multi-stage generative adversarial network (GAN) framework for efficient high-resolution prediction of received power map in indoor office environments. The model integrates three GAN architectures: Deep Convolutional GAN (DCGAN), Cycle-Consistent GAN (CycleGAN), and Super-Resolution GAN (SRGAN), forming a sequential pipeline. Initially, DCGAN is used to generate low-resolution received power map samples, enhancing the limited simulation data. CycleGAN then refines these samples into high-resolution representations, while SRGAN is trained on the augmented low-high resolution pairs to enhance the high-resolution data, producing high-fidelity received power map predictions. The proposed method significantly reduces computational overhead compared to traditional high-resolution simulations, while achieving comparable accuracy. The framework is adaptable to new environments without extensive pre-training, demonstrating strong generalization to unseen spatial domains.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".