Channel Aware Scheduling Algorithm for LTE Uplink and Downlink
Bibliographic record
Abstract
In the past two decades, there has been a drastic increase in the mobile traffic, which is caused by the improved user experience with smart phones and its applications. In LTE system, the packet scheduler plays a vital role in the effective utilization of the resources. This field is not standardized and has immense scope of improvement, allowing vendor-specific implementation. LTE scheduling can be categorized into two extremes, namely, Opportunistic scheduling and Fairness scheduling. The Best Channel Quality Indicator (BCQI) algorithm falls under the former category while Proportional Fairness (PF) algorithm under the later. BCQI algorithm provides high system throughput than PF algorithm, however, unlike BCQI algorithm, PF algorithm considers users with poor channel condition for allocation process. In this work, a new scheduling algorithm called as Opportunistic Dual Metric (ODM) Scheduling Algorithm is proposed for LTE uplink and downlink.The objective of the algorithm is to prioritize the users with good channel condition for resource allocation, at the same time not to starve the users with poor channel conditions. The proposed algorithm has two resource allocation matrices, one being throughput-centric and the other being is fairness-centric. The uplink algorithm uses the two resource allocation matrices to allocate the resources to the users and to ensure contiguous resource allocation. The downlink algorithm is an extension of the proposed uplink algorithm avoiding uplink constraints. The downlink algorithm employs the two resource distribution matrices to provide an efficient resource allocation by expanding the allocation for the users considering intermittent resources. The performance of ODM is measured in terms of throughput, fairness. Additionally, the uplink algorithm is analyzed in terms of transmit power. From the results it is observed that the proposed algorithm has better trade-off in terms of all the performance parameters than PF scheduler and BCQI scheduler.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".