Ergodic capacity analysis of downlink distributed antenna systems using stochastic geometry
Bibliographic record
Abstract
This paper studies the ergodic capacity of a multicell distributed antenna system (DAS), where remote antenna ports are spread within each cell to cooperatively transmit to user terminals. Unlike most prior studies which assume the antenna ports to be deployed at fixed locations, this paper assumes the antenna ports to be distributed as a spatial Poisson point process (PPP) to account for the fact that in practice the antenna ports are randomly placed to cover wherever the dead spots are. We first model DAS within each cell as a downlink multiple-input single-output (MISO) channel with per-antenna power constraint while accounting for inter-cell (inter-cluster) interference. Two DAS layouts are considered: the “regular” layout where the antenna ports are randomly deployed within regular cellular boundary to serve a given user, and the “user-centric” layout where the antenna ports are distributed over a wide area and the users choose the surrounding antenna ports to form a “virtual cell” as its own serving antenna subset. Using the tool of stochastic geometry, we analytically derive efficiently computable ergodic capacity expressions for the two layouts of DAS. Using these expressions, the cell-edge capacity of DAS under the regular layout is shown to be upper-bounded by α/2, where α is the pathloss exponent. Numerical results show that the proposed analytical model can accurately model the first layout, and can well approximate the second layout when the serving radius of users is not large. Compared to the traditional cellular system where all antennas are co-located at the cell center, DAS has better cell-edge performance. Further, the user-centric DAS has higher capacity than the DAS under regular layout.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".