Energy Efficiency Maximization for Downlink NOMA Designs via Symbol-Level Precoding
Bibliographic record
Abstract
The multi-antenna non-orthogonal multiple access (NOMA) technique is a promising method to enhance energy and spectrum efficiencies of wireless communication systems through advanced precoding algorithms. However, conventional NOMA schemes encounter high complexity issues due to the successive interference cancellation (SIC) process at the receiver end. Moreover, traditional precoding designs for multi-antenna NOMA systems only utilize user channel state information and overlook the modulation details of transmitted data symbols, which may result in suboptimal performance. To overcome these disadvantages, we propose a symbol-level precoding (SLP) scheme to maximize the energy efficiency of the downlink NOMA systems. In particular, by the proposed SLP scheme, the “interference signals” is designed to fall within the decoding region of the “desired signal”, eliminating the need for an SIC receiver, thereby reducing the complexity of the NOMA system in practical applications. For ease of illustrating the idea of the SLP-based NOMA designs, we first focus on a single user group case with two users, and solve the associated problem by developing a fractional programming-based algorithm. Then, we extend the idea to the multi-user scenarios and focus on the joint user grouping and SLP design. For the purpose of computational complexity reduction, a low-complexity (adaptive) user grouping scheme is proposed based on the idea of unsupervised clustering. Finally, numerical simulations are carried out to validate the effectiveness of the proposed SLP scheme and algorithms in improving the energy efficiency of the system.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".