Opportunistic Nonorthogonal Packet Scheduling in Fixed Broadband Wireless Access Networks
Bibliographic record
Abstract
In order to mitigate high cochannel interference resulting from dense channel reuse, the interference management issues are often considered as essential part of scheduling schemes in fixed broadband wireless access (FBWA) networks. To that end, a series of literature has been published recently, in which a group of base stations forms an interferer group (downlink transmissions from each base station become dominant interference for the users in other in-group base stations), and the scheduling scheme deployed in the group allows only one base station to transmit at a time. As a result of time orthogonality in transmissions, the dominant cochannel interferers are prevented, and hence the packet error rate can be improved. However, prohibiting concurrent transmissions in these orthogonal schemes introduces throughput penalty as well as higher end-to-end packet delay which might not be desirable for real-time services. In this paper, we utilize opportunistic nonorthogonality among the in-group transmissions whenever possible and propose a novel transmission scheduling scheme for FBWA networks. The proposed scheme, in contrast to the proactive interference avoidance techniques, strives for the improvements in delay and throughput efficiency. To facilitate opportunistic nonorthogonal transmissions in the interferer group, estimation of signal-to-interference-plus-noise ratio (SINR) is required at the scheduler. We have observed from simulations that the proposed scheme outperforms the reference orthogonal scheme in terms of spectral efficiency, mean packet delay, and packet dropping rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".