Transmission and Clustering Designs for Multi-Antenna NOMA Based on Average Transmit Power
Bibliographic record
Abstract
In this paper, we analyze the average transmit power of non-orthogonal multiple access (NOMA) systems with requirements on the user signal-to-interference-plus-noise-ratios (SINRs), and propose new transmission schemes and user clustering algorithms to reduce the average transmit power. First, for systems with a multi-antenna base station (BS) and a single cluster of two single-antenna users, the average transmit power with SINR constraints is analyzed for channel-alignment-based NOMA and multi-user beamforming with matched filter (MF) beamformers. Properties of the average transmit power are obtained with respect to the alignment threshold and the BS antenna number. It is shown that using either scheme alone without the alignment consideration leads to unbounded average transmit power. With the observation that the two schemes have distinct preferred regions of channel alignment, hybrid transmissions of NOMA and multi-user beamforming are proposed for power saving and outage avoidance. Further, for systems with more than two users, clustering algorithms are developed to group users into multiple two-user clusters with respect to the minimization of the total transmit power for NOMA and hybrid schemes. Simulation results are provided to validate our theoretical results. In addition, the proposed transmission schemes and clustering algorithms are shown to achieve significant saving in the average transmit power.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".