Dynamic configuration of single frequency networks in mobile streaming systems
Bibliographic record
Abstract
Although the capacity of cellular networks has increased with recent generations, the growth in demand of wireless bandwidth has outpaced this increase in capacity. Not only more users are relying on wireless networks, but also the demand from each user has substantially increased. For example, it has become common for mobile users to stream full TV episodes, sports events, and movies while on the go. Further, as the capabilities of mobile devices improve, the demand for higher quality and even 3D videos will escalate, which will strain cellular networks. Therefore, efficient utilization of the expensive and limited wireless spectrum remains an important problem, especially in the context of multimedia streaming services that consume a large portion of the wireless capacity. In this paper, we introduce the idea of dynamically configuring cells in wireless networks to form single frequency networks based on the multimedia traffic demands from users in each cell. We formulate the resource allocation problem in such complex networks with the goal of maximizing the number of served multimedia streams. We prove that this problem is NP-Complete, and we propose a heuristic algorithm to solve it. Through detailed packet-level simulations, we show that the proposed algorithm can achieve substantial improvements in the number of streams served as well the energy saving of mobile devices. For example, our algorithm can serve up to 40 times more users compared to the common unicast streaming approach, and it achieves at least 80% and up to 400% improvement compared to multicast approaches that do not use single frequency networks.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".