Economics of user-in-the-loop demand control with differentiated QoS in cellular networks
Bibliographic record
Abstract
Increasing cellular traffic is the driving force for innovations in wireless communications. While voice traffic is not expected to increase much and does not require 4G systems, traffic for video and data applications is expected to grow with a rate of 100% per year. Smart mobile devices, tablets and laptop dongles will certainly make this a reality. On the other hand the supply side cannot grow with the same rate. Base stations, eNB, pico- and femtocells will bring more heterogeneity in space and new applications will bring more heterogeneity in demand over time. Designing for over-provisioning capacity has been the standard approach to stabilize traffic, but is will be harder and harder, with more congestion situations in time (busy hour) and space (crowded cell) which will break application traffic and give bad quality-of-experience of users. Furthermore, over-provisioning comes with more power consumption and higher financial expenditures for infrastructure and operating costs. The user-in-the-loop (UIL) approach offers a solution orthogonal to the traditional supply-only view. In addition to technical improvements, having a temporal demand control can alleviate the severity of busy-hour situations which formerly caused congestion and connection failures. Demand shaping is implemented by a dynamic usage-based tariff and adaptive rates depending on the load condition. The users in a cell are part of a closed control loop which reacts in cases of severe demand overload. In this paper three different service classes are controlled individually and results from analysis and simulation show the performance in stationary and dynamic scenarios. The economics of tariffs and dynamic prices and the resulting operator revenue on one side is compared to the dissatisfaction of rejected users and this gives decision indicators for the investment into new infrastructure. Overall this saves money, energy and turns situations of hard congestion into an elastic stationarity which is in the interest of both users and operators.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".