Multi-Cell Over-the-Air Computation Systems With Spectrum Sharing: A Perspective From $\alpha$-Fairness
Bibliographic record
Abstract
Wireless data aggregation (WDA) is a pivotal enabling technique in the era of Internet of Things (IoT). Recently, an emerging WDA method, over-the-air computation (AirComp), has been proposed to perform fast data aggregation, with improved spectrum utilization and shortened transmission delay. In this article, we consider a multi-cell AirComp system with single-input multiple-output (SIMO) communications. All users in the system share the same wireless spectrum. The mean squared error (MSE) is used as the metric to quantify the computation accuracy of each cell. To achieve the fairness of data aggregation among different cells, we formulate a unified optimization problem to minimize the MSE-based objective function, from the perspective of$\alpha$-fairness. Due to the non-convexity issue, the optimization problem is first divided into four cases according to the choice of$\alpha$. Thereafter, an efficient solution based on the block coordinate descent (BCD) method is proposed for each case. Specifically, in the first case, the optimal solutions are obtained for the beamforming at the AP and the power allocation at the user. In the second and third cases, a tractable solution to determine the power allocation is devised by adopting the successive convex approximation (SCA) method. In the last case, the majorization minimization (MM) method is applied to tackle the power allocation problem. Furthermore, by analyzing the MSE performance, we arrive at an in-depth insight. Namely, the impact of the inter-cell interference disappears when the number of antennas at the AP is sufficiently large. The effectiveness of our proposed schemes is validated by the numerical results.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".