Efficient Antenna Selection and User Scheduling in 5G Massive MIMO-NOMA System
Why this work is in the frame
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Bibliographic record
Abstract
To achieve extremely high spectral efficiency in the 5- th generation (5G) communication network, the combination of massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technologies becomes a promising solution. However, due to limited radio frequency (RF) chains and channel condition variation, it is important to develop efficient antenna selection and user scheduling algorithms in complex MIMO-NOMA system. In this paper, efficient antenna selection and user scheduling algorithms are investigated to maximize the sum rate in two MIMO-NOMA scenarios. In the first simple single- band two-user scenario, the proposed antenna selection algorithm achieves higher search efficiency by limiting the candidate antennas to those are beneficial to the relevant users. In the multi-band multi-user scenario, the proposed joint antenna and user (AU) contribution algorithm considers the contribution of each antenna's and user's channel gain to total channel gain jointly. Numerical results show that proposed antenna selection algorithm achieves near-optimal performance, and joint AU contribution algorithm achieves similar performance to existing methods with reduced complexity.
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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 | 0.000 | 0.000 |
| 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 it