On the Resource Allocation and User Association in Future Multi-Band Wireless Networks
Why this work is in the frame
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Bibliographic record
Abstract
In response to the challenges of spectrum scarcity and the exponential growth of the number of connected devices, this paper addresses the joint optimization problem of user-base station association, channel assignment and power allocation in a multi-band wireless network, where sub-6 GHz, millimeter wave, and terahertz frequency bands coexist. The problem is formulated as a mixed integer non-linear programming, a known NP-hard problem. Each user requests both a minimum data rate and a minimum reliability level defined by a signal-to-noise ratio. Considering the goal of optimizing the number of satisfied users, this paper proposes a multi-agent deep reinforcement learning solution. Simulation results convincingly demonstrate the effectiveness of our proposed algorithm and its ability to learn fast the best resource allocation solution.
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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 | 0.000 | 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