Achievable Rate Characterization of NOMA-Aided Cell-Free Massive MIMO With Imperfect Successive Interference Cancellation
Bibliographic record
Abstract
This paper investigates the throughput improvement of cell-free massive multiple-input multiple-output (MIMO) systems by non-orthogonal multiple access (NOMA) for future cellular networks under stochastic access point and user locations. In this context, the node locations are modeled with Poisson point processes. The time division duplexing mode is employed, and uplink channels are estimated locally using uplink pilots. Furthermore, unique pilot sequences are used between NOMA clusters, while pilot reuse occurs within each cluster to strike a balance between the training overhead and the number of clusters. Matched-filter-based precoding is utilized for downlink transmission. The aggregate received signal is analytically characterized by deriving the moment generating function and approximations via moment matching. Then, the asymptotic achievable rates of the NOMA users are derived, thereby quantifying the adverse impact of error propagation owing to imperfect successive interference cancellation. Special scenarios with prior downlink channel state information and log-distance power control are also considered. We show that NOMA greatly increases the achievable average rate, especially under low path loss exponents and dense networks, while user fairness may be boosted by the adoption of a log-distance transmit power control scheme with proper parameter selection (i.e. lower values for the power control parameter).
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| 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".