Optimal Resource Allocations for Mobile Data Offloading via Dual-Connectivity
Bibliographic record
Abstract
The rapid growth of mobile traffic has heavily overloaded the cellular networks, making it increasingly desirable to offload mobile users' (MUs') traffic to small-cell networks. In this paper, we study the MUs' optimal uplink traffic offloading scheme based on the new paradigm of small-cell dual-connectivity (DC). Through DC, an MU can flexibly schedule its traffic between a macro-cell base station (BS) and a small-cell access point (AP) via two different radio interfaces. To optimize the overall network radio resource usage, we jointly optimize the BS' bandwidth allocation as well as the MUs' traffic scheduling and power allocation. Specifically, for reducing the bandwidth usage, the BS prefers to allocate the MUs small amount of bandwidth to encourage the MUs to utilize the small-cell networks. However, excessive traffic offloading can lead to severe interferences among MUs, which increase the MUs' power consumption. Hence, our joint optimization strikes a proper balance between these two aspects. Despite the non-convexity of the proposed joint optimization problem, we propose an efficient algorithm to compute the optimal offloading solution. The key idea is to exploit the layered-structure of the joint optimization problem, and decompose it into the BS' bandwidth allocation problem (on the top-level) and the MUs' traffic scheduling and power allocation problem (as a subproblem). Such a decomposition enables us to exploit the hidden convexity of the MUs' problem and the monotonic structure of the BS' problem for an effective algorithm design. Numerical results show that our proposed algorithm can achieve the global optimum solution with significantly reduced computational time. Moreover, the proposed traffic offloading scheme can significantly reduce the overall system cost, in comparison with using the fixed bandwidth allocation or traffic scheduling schemes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".