Spectrum-efficient topology management of asymmetric interference alignment networks
Bibliographic record
Abstract
Interference alignment (IA) is a promising technique in wireless networks. However, existing works are mostly based on symmetric IA networks. To meet the requirements of practical applications, we consider asymmetric IA networks based on the various pathloss. In this paper, a spectrum-efficient topology management scheme is proposed for the asymmetric IA networks. In the scheme, for the user far away from others, solely adopting spatial multiplexing (SM) as a point-to-point subnetwork is more spectrum-efficient. On the other hand, for the others aggregating together, jointly comprising an IA subnetwork may be a better choice. We first present the criterion to decide which is more spectrum-efficient for the topology management scheme, i.e., IA or SM. Then, the topology management scheme is elaborated with the graph theory. In addition, the designs of the precoding and decoding matrices are presented in the IA and SM schemes, respectively. Simulation results show that the proposed topology management scheme is much more spectrum-efficient than the conventional IA scheme in the asymmetric multiuser network.
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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 | 0.001 | 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".