Wireless Semantic Communication in MIMO Systems: A Probability Distribution Approach
Bibliographic record
Abstract
This paper proposes a semantic communication design for multiple-input multiple-output (MIMO) systems, integrating stochastic MIMO channels and stochastic semantic features based on probability density modeling. Firstly, the semantic features are modeled as a set of independent univariate Gaussian random variables, using the variational inference technique. Then, by directly transmitting the semantic features over the stochastic MIMO channels, the optimization problem is formulated as the joint problem of symbol transmission and channel adaptation to minimize the average energy consumption subject to the semantic fidelity constraint. Finally, a joint symbol transmission and channel adaptation (JSCA) algorithm is proposed for obtaining the optimal solution of joint symbol transmission and channel adaptation. Simulation results on the image classification task show that the proposed JSCA algorithm maintains a relatively high classification accuracy across all settings (above 0.96 on MNIST and 0.85 on CIFAR-10), while the baseline (the traditional digital communication scheme) degrades sharply under low signal-to-noise ratio (SNR) or high-order modulation. Moreover, the proposed JSCA algorithm can reduce the energy consumption on the image classification task by up to 99.5 % (MNIST) and 98.8 % (CIFAR-10), compared with the traditional digital communication scheme.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".