Interference Avoidance through Dynamic Downlink OFDMA Subchannel Allocation using Intercell Coordination
Bibliographic record
Abstract
OFDMA subchannel allocation has been well-studied. However, most of the available literature considers a single cell system without cochannel interference. We present a novel intercell interference avoidance scheme, in which the downlink OFDMA subchannels are allocated through intercell coordination in a multicell environment. The proposed scheme not only aims to achieve maximized network throughput but also focuses on providing improved throughput for cell or sector edge users that are most affected by intercell interference. Enhanced cell edge performance may result in fewer base stations needed to cover a region; this, in turn, may yield substantial savings in deployment cost. The scheme is comprised of two different algorithms; one at the base station level, and the other at a central controller to which a group of neighboring base stations are connected. The performance of the proposed scheme is compared with that of a reference scheme in which coordination is not employed. It is observed from simulation results that the proposed scheme outperforms the reference scheme in terms of network and cell edge throughput.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".