Modeling and Analysis of User-centric and Disjoint Cooperation in Network MIMO Systems
Bibliographic record
Abstract
This thesis provides an analytical performance characterization of user-centric and disjoint cooperation for both uplink (UL) and downlink (DL) network multiple-input multipleoutput (MIMO) systems, where base-stations (BSs) form clusters to jointly transmit information to and receive information from multiple mobile users. We specically focus on the user-centric BS clustering strategy in which cooperating cluster of neighboring BSs is formed for each user individually and the clusters for dierent users may overlap. We consider the random topology where the BSs and users form Poisson point processes to facilitate analysis. This thesis shows that as compared to with disjoint BS clusters, the eect of user-centric clustering is that it improves signal strength in both UL and DL, while reducing cluster-edge interference in the DL. As a result, user-centric clustering has larger ergodic rate gain in the UL than DL, while it has signicant rate gain for cluster-edge users in the DL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".