Multimedia Content Delivery in Millimeter Wave Home Networks
Bibliographic record
Abstract
Millimeter wave (mm-wave) communication is a promising technology for short-range communications and high-speed services. It provides potential solutions for multimedia content delivery in home networks. However, approaches for resource allocation in traditional wireless networks may not be efficient for multimedia data transmissions in mm-wave networks. This is due to the very wide bandwidth and highly directional transmissions, which bring challenges as well as opportunities to the resource allocation of mm-wave transmissions. In this paper, we first characterize different usage scenarios of multimedia content delivery by introducing a set of utility functions. We then formulate a joint power and channel allocation problem based on a network utility maximization framework, which captures the spatial and frequency reuse of mm-wave communications. The formulated problem is a non-convex mixed integer programming (MIP) problem. We reformulate the problem into a convex MIP problem and propose a resource allocation algorithm based on outer approximation (OA) method. We further develop an efficient heuristic algorithm, which has a lower complexity than the OA based algorithm. Simulation results present the tradeoffs between the OA based and heuristic algorithms for different scenarios and show that our proposed algorithms substantially outperform recently proposed schemes in the literature.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".