Optimal Resource Allocations for Mobile Data Offloading via Dual-Connectivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it