Resource Allocation in Topology Management of Asymmetric Wireless Interference Networks
Bibliographic record
Abstract
Most research works of interference alignment (IA) focus on the symmetric networks. When the difference of path loss is considered in asymmetric networks, topology management (TM) needs to be carefully designed for IA-based interference networks, through separating the network into an IA subnetwork and some spatial multiplexing (SM) subnetworks. Nevertheless, the resource allocation problem has been largely ignored in previous works on TM for IA-based networks. In this paper, antenna selection (AS) and power allocation (PA) are exploited to further improve the performance of IA- based networks. We first apply AS technique to the IA subnetwork, through fully utilizing the redundant antennas. Then the transmitted power is allocated among the transmitters of both the IA and SM subnetworks, to optimize the spectrum efficiency. Based on these two techniques, the joint optimization of AS and PA is developed through a stepped resource allocation optimization strategy to further improve the performance with low computational complexity. Simulation results are presented to show the effectiveness of the proposed schemes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".