Radio resource allocation in heterogeneous wireless networks: A spatial-temporal perspective
Bibliographic record
Abstract
We study optimal radio resource allocation across multiple tiers of a heterogeneous wireless network in order to maximize the downlink sum throughput. Different from prior works, we consider both the randomness of base stations in space and dynamic user traffic session arrivals in time, accounting for both elastic and inelastic user traffic. A new stochastic analysis framework, which accommodates both spatial and temporal dimensions, is proposed to quantify the throughput objective. The derived throughput function is not in closed form and is non-concave in terms of the radio resource allocation factors to be optimized, hindering the search for an efficient optimization solution. Therefore, we further develop closed-form concave bounds that envelop the throughput function, to form convex approximations of the original optimization problem that can be solved efficiently. We characterize the performance gap when these bounds are used instead of the original objective. Both analytical bounding and simulation experiments demonstrate that the proposed solution is nearly optimal.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".