Downlink Spectral Efficiency of Distributed Antenna Systems Under a Stochastic Model
Bibliographic record
Abstract
This paper studies the downlink spectral efficiency of distributed antenna system (DAS) where antenna ports are distributed as a Poisson point process (PPP), while assuming channel state information is not available at the transmitter and each antenna has an individual power constraint. We first consider the case with a single user per cell and analyze regular DAS with fixed cell boundaries, and study both blanket transmission where the user is served by all the antenna ports within each cell, and selective transmission where only the closest antenna port to the user within each cell is selected. We derive efficiently computable spectral efficiency expressions as a function of the user location, and show the limitation of blanket transmission by establishing that the cell-edge spectral efficiency under blanket transmission is upper bounded by a constant. Further, from a network perspective, we also model users as a PPP and assume a time-division multiple-access (TDMA) scheme, and give analytical expressions for and compare the average spectral efficiencies of regular DAS and user-centric DAS where no fixed cell boundaries exist. We validate our models with simulation, and show that selective transmission outperforms blanket transmission for regular DAS, and user-centric DAS with selective transmission achieves a higher spectral efficiency averaged over the network than regular DAS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".