Energy-efficient resource scheduling for NOMA systems with imperfect channel state information
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Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) is considered as a promising technology for the fifth generation mobile communications. Energy-efficient resource allocation scheme is studied for a downlink NOMA wireless network, where multiple users can be multiplexed on the same subchannel by applying successive interference cancellation technique at the receivers. Most previous works focus on resource allocation for sum rate maximization with perfect channel state information (CSI) in NOMA systems. We formulate the energy-efficient resource allocation as a probabilistic mixed non-convex optimization problem by considering imperfect CSI. To solve this problem, we decouple it into user scheduling and power allocation sub-problems. We propose a low-complexity suboptimal user scheduling algorithm and a power allocation scheme to maximize the system energy efficiency under the maximum transmitted power limit, imperfect CSI and the outage probability constraints. Simulation results are provided to show that the proposed algorithms yield much improved energy efficiency performance over the conventional orthogonal frequency division multiple access scheme.
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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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