Interference Management in Full-Duplex Wireless Cellular Networks via Fractional Programming - Invited Paper
Bibliographic record
Abstract
Mutual interference is a key obstacle in the realistic adoption of full-duplex (FD) technique in future wireless cellular networks. Interference is a much more pressing problem for FD system than for the conventional half-duplex (HD) system, because FD allows the same time-frequency resource to be used for both uplink and downlink, thus possibly creating myriad interference between multiple transmissions throughout the network. Without proper interference control, FD may not even outperform HD in a multicell setup. The main objective of this paper is to show that coordinated scheduling and power control enables wireless cellular networks to reap significant system-level performance improvement due to FD. Toward this end, this paper utilizes fractional programming to derive a sequence of convex reformulations that allow distributed and efficient iterative optimization. Numerical results suggest that the proposed system-level interference management can provide 30-40% rate gain for an optimized FD multicell network as compared to optimized HD.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".