Designing and optimizing swarming in a distributed base station network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Todays' networks are becoming increasingly complex. They must provide a growing variety of services to a wide range of devices. In order to do so, they must make efficient use of modern technologies including MIMO, macrodiversity, power control, channel allocation, beamforming, and so on. In this context, the centralized management of radio resources on a large scale is rapidly becoming intractable. Distributed intelligence constitutes an increasingly attractive solution to provide network-wide self-configuration and adaptation capabilities. This article presents the design of a swarming system for autonomous power control which adapts naturally to the changing conditions of mobile networks where interference patterns are in constant flux. Empirical methods proposed by Parunak [1997] to develop MultiAgent Systems with Swarming (MASS) are applied to the current context while emphasizing the key concepts that lead to swarming (emergent behavior). A simulation-based study reveals how the system can be fine-tuned to obtain various solutions, balancing resources differently to achieve different trade-off points. Finally, it is shown that the distributed approach based on swarming is not only feasible but leads to higher global QoS levels than comparable centralized approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it