Dynamic Resource Allocation for LTE-Based Vehicle-to-Infrastructure Networks
Bibliographic record
Abstract
This paper studies the dynamic resource allocation (DRA) problem for LTE-based vehicle-to-infrastructure networks, where the goal is to minimize the total power consumption (TPC) in the downlink, subject to both power constraints and rate requirements. Under time-varying channel conditions, the TPC minimization takes the form of a discrete-time sequence of NP-hard combinational optimization problems. To solve these sequential problems, we propose a novel two-stage algorithm, named as DRA and precoding algorithm (DRA-Pre). In the first stage, the resource allocation problem (i.e., pairing of vehicle users to roadside units, and subcarrier allocation) is solved by applying the multi-value discrete particle swarm optimization method. This approach takes advantage of the channel correlation by exploiting the relationship between resource allocation solutions in adjacent time slots, which can improve the TPC performance. In the second stage, the precoding design problem is solved by a low-complexity algorithm, where the original problem is split into two subproblems, i.e., a rate max-min subproblem and a TPC minimization subproblem. Simulation results show that the proposed algorithm converges rapidly and significantly outperforms benchmark approaches in terms of TPC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".