Bibliographic record
Abstract
This paper provides an analytical performance characterization of user-centric cooperation for network multiple-input multiple-output (MIMO) systems, where base-stations (BSs) form finite-sized clusters to jointly transmit information to and receive information from multiple mobile users. In the user-centric model, the cooperation BS cluster for each user is formed individually and may overlap with each other. The size of clusters determines the amount of backhaul and channel state information needed for implementation. The BSs are equipped with multiple antennas; multiple single-antenna users are served simultaneously; the cooperating BSs perform zero-forcing beamforming across the cluster. By using a stochastic geometry model where the BSs and the users form Poisson point processes over the two-dimensional plane and by further approximating both the signal and interference powers using Gamma distributions of appropriate parameters, this paper shows that, network MIMO provides sum-rate gain for both uplink (UL) and downlink (DL) transmission as compared to single-cell processing. The sum-rate gain is about 30%-60% for a cluster size of 10 and is larger in DL than UL in a typical deployment due to the larger DL transmit power. More significantly, network MIMO can provide 300% gain or more for cluster-edge users, but only for the DL and only with user-centric clustering. This highlights the conclusion that performance evaluation for network MIMO should focus on DL cluster-edge users and on the user-centric clustering strategy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".