User-in-the-loop: spatial and temporal demand shaping for sustainable wireless networks
Bibliographic record
Abstract
The demand for wireless access data rates is growing exponentially at a pace where supply cannot keep up with. Wireless resources (spectrum, time, space) are limited and shared, and transmission rates cannot be improved anymore solely with physical layer innovations. On the consumer side, flat rate type tariffs have established unnecessarily high expectations and often wasteful consumption. Dealing with congestion is unavoidable as a consequence of operating in a regime where demand is close to, equal to, or exceeding the supply. We can no longer assume that the current over-provisioning approach continues to be feasible. Complementary to the engineering for the growth of the supply side, this article focuses on the engineering for the control of the demand side. An approach referred to as the “user-in-the-loop” (UIL) is therefore motivated here. This article proposes spatial control, in which the user is encouraged to move to a less congested location, and temporal control, in which incentives (e.g., dynamic pricing) ensure that the user reduces (or postpones) his current data demand in case the network is congested. Results from a survey, which measures how willing a user is to respond to such control, are also presented. As users are modeled by a system-theoretic box in a closed-loop (control) system, they feature an input handle for incentives and an output handle for the reaction. Incentives can be progressive tariffs, reward programs, higher access rates, or even environmental (green) indicators. Incentives are tailored to the major Quality-of-Service (QoS) classes and help to shape the demand at the application layer-7 as well as at the user (“layer-8”). UIL can safely be applied in addition to other technologies, which are mainly for increasing the supplied capacity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".