Distributed Resource Allocation in D2D-Enabled Multi-tier Cellular\n Networks: An Auction Approach
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
Future wireless networks are expected to be highly heterogeneous with the\nco-existence of macrocells and small cells as well as provide support for\ndevice-to-device (D2D) communication. In such muti-tier heterogeneous systems\ncentralized radio resource allocation and interference management schemes will\nnot be scalable. In this work, we propose an auction-based distributed solution\nto allocate radio resources in a muti-tier heterogeneous network. We provide\nthe bound of achievable data rate and show that the complexity of the proposed\nscheme is linear with number of transmitter nodes and the available resources.\nThe signaling issues (e.g., information exchange over control channels) for the\nproposed distributed solution is also discussed. Numerical results show the\neffectiveness of proposed solution in comparison with a centralized resource\nallocation scheme.\n
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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