Performance analysis of SDMA with inter-tier interference nulling in HetNets
Bibliographic record
Abstract
The downlink performance of two-tier (macro/pico) multi-antenna cellular heterogeneous networks (HetNets) employing space division multiple access (SDMA) technique is analyzed in this paper. The number of users simultaneously served with SDMA by each BS in a resource block depends on user distribution, unlike previous studies which assume the number to be any arbitrary value. By exploiting the feasibility of deploying larger number of antennas at macro BS, we propose to utilize the excess spatial degrees of freedom for interference nulling to pico users from their corresponding nearest (dominant) macro BSs. Biased-nearest-distance based user association scheme is proposed as those introduced in previous studies are unsuitable for analyzing the proposed multi-antenna scheme. Coverage probability and average data rate of a typical user are then evaluated. Our results demonstrate that the proposed interference nulling scheme has strong potential to improve performance. However, the system parameters such as association bias, and number of dedicated antennas at each macro BS for serving its own users must be carefully tuned.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".