Power Control and Performance Analysis of Outage-Limited Cellular Network with MUD-SIC and Macro-Diversity
Bibliographic record
Abstract
In this paper, we analyze the uplink goodput (bits/sec/Hz successfully decoded) and per-user packet outage in a cellular network using multi-user detection with successive interference cancellation (MUD-SIC). We consider non-ergodic fading channels where microscopic fading channel information is not available at the transmitters. As a result, packet outage occurs whenever the data rate of packet transmissions exceeds the instantaneous mutual information even if powerful channel coding is applied for protection. We are interested to study the role of macro-diversity (MDiv) between multiple base stations on the MUD-SIC performance where the effect of potential error-propagation during the SIC processing is taken into account. While the jointly optimal power and decoding order in the MUD-SIC are NP hard problem, we derive a simple on/off power control and asymptotically optimal decoding order with respect to the transmit power. Based on the information theoretical framework, we derive the closed-form expressions on the total system goodput as well as the per-user packet outage probability. We show that the system goodput does not scale with SNR due to mutual interference in the SIC process and macro-diversity (MDiv) could alleviate the problem and benefit to the system goodput.
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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 | 0.005 |
| 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".