Outage Probability Analysis and Resolution Profile Design for Massive MIMO Uplink With Mixed-ADC
Bibliographic record
Abstract
This paper analyzes the outage probability for the uplink of multi-user massive multi-input-multi-output systems with a mixed analog-to-digital converter (ADC) architecture, in which the base station (BS) is equipped with ADCs of different resolution levels. Maximum-ratio combining (MRC) is used at the BS. By deriving the distribution of the user-interference power and statistical properties of other components in the signal-to-interference-plus-noise-ratio (SINR), a tight closed-form approximation for the outage probability is obtained for a general mixed ADC structure with any resolution profile. Then, two methods for the ADC resolution profile optimization are proposed considering both the outage probability and the BS energy consumption. The first method uses low-complexity incremental search to minimize the BS energy consumption for given outage probability constraint. The other method is based on multi-objective optimization and adopts a discrete-variation of the classic non-dominated sorting genetic algorithm II (NSGA-II). Numerical results are presented to validate the outage probability results. Furthermore, it is shown that the two proposed mixed-resolution ADC designs largely outperform a two-level ADC structure and provide more choices than the uniform ADC structure for resolving the tradeoff between outage probability and BS energy consumption.
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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 | 0.006 |
| 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 | 0.001 |
| 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".