Air-ODE Neural Network with Distributed RISs Aided Communication Systems
Bibliographic record
Abstract
Analog machine-learning hardware platforms promise to be faster and more energy efficient than their digital counterparts. Specifically, over-the-air analog computation allows offloading computation to the wireless environment through carefully constructed transmitted signals. In addition, reconfigurable intelligent surface (RIS) is emerging as a promising solution for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Based on the merits brought by the RIS, we design and implement the residual-based block that uses over-the-air computation and demonstrate it for inference tasks in an ordinary differential equation (ODE) deep neural network. We engineer the ambient wireless propagation environment through distributed RISs to design such an architecture, which is termed as over-the-air ordinary differential equation (Air-ODE) neural network. In contrast to the conventional digital ODE-inspired neural network architecture, the Air-ODE block leverages the physics of wave reflection and the reconfigurable phase shifts of RISs to implement an ODE-based block in the analog domain. We then validate the entire Air-ODE neural network based on a complex-valued image reconstruction task. The simulation results illustrate that the analog Air-ODE can achieve similar performance to the digital ODE network and the deployment of the Air-ODE block can achieve 2.08 times and 1.29 times performance gain on peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), respectively.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".