Designing and optimizing swarming in a distributed base station network
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".