Energy-efficient resource scheduling for NOMA systems with imperfect channel state information
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".