Generalized Queue-Aware Resource Management and Scheduling for Wireless Communications
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 general problem of a queue-aware radio resource management and scheduling design is investigated for wireless communications under quasi-static fading channel conditions. Based on an analysis of the source buffer queuing system, the problem is formulated as a constrained nonlinear discrete programming problem. The state transition matrix of the queuing system determined by the queue-aware scheduler is shown to have a highly dynamic structure, so that the conventional matrix analysis and optimization tools are not applicable. By reformulating the problem into a nonlinear integer programming problem on an integer convex set, a direct search approach is considered. Two types of search algorithms, gradient based and gradient-free, are investigated. An integer steepest-descent search with a sub-sequential interval search algorithm and a constrained discrete Rosenbrock search (CDRS) algorithm is proposed to solve the nonlinear integer problem. Both algorithms are shown to have low complexity and good convergence. The numerical results for a single user resource allocation are presented, which show that both algorithms outperform equal partitioning and random partitioning queue-aware scheduling. The dynamic programming (DP) solution given by the relative value iteration algorithm, which provides the true optima but has high complexity, is used as a benchmark. In the majority of the numerical examples, the performance of the CDRS algorithm is almost identical to that of the DP approach in terms of both the average queue length minimization and the average packet blocking plus packet retransmission minimization, but it is less complex, and thus has better scalability.
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 | 0.000 | 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