Rate and Energy Efficiency Improvements of Massive MIMO-Based Stochastic Cellular Networks With NOMA
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) coupled with massive multiple-input multiple-output (MIMO) base stations hold immense potential in increasing the spectral and energy efficiencies while enabling massive access of future cellular networks. To this end, we investigate the rate performance of a system employing multi-user NOMA and massive MIMO base stations distributed under a Poisson process. We adopt a time-division-duplexing mode, and employ matched filter based precoding in the downlink of the cellular network. We investigate two power allocation scenarios for the individual users while keeping the overall power usage of a NOMA cluster constant: 1) pre-defined power allocation and 2) power allocation based on user-base station distance. Considering imperfect successive interference cancellation, pilot contamination, and error propagation for the two power allocation scenarios, we characterize the average rate of a NOMA user and the signal detection probability under asymptotic conditions for the number of antennas. Furthermore, we derive the moment generating function of the out-of-cell interference caused by pilot contamination. We show that NOMA improves the rate performance and by extension the energy efficiency under most system conditions. Moreover, the rate can be increased further through denser network deployments, while the user fairness is greatly impacted by the power allocation scheme.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".