Joint Optimization Framework for User Clustering, Downlink Beamforming, and Power Allocation in MIMO NOMA Systems
Bibliographic record
Abstract
In this paper, we investigate the application of downlink beamforming along with non-orthogonal multiple access (NOMA) in a multi-user multiple-input multiple-output (MIMO) system. The joint optimization framework for user clustering, downlink beamforming and power allocation scheme is formulated as a novel mixed-integer non-linear program (MINLP), where the aim is to minimize the total transmission power while satisfying quality-of-service (QoS), user clustering and power constraints. Owing to the non-convexity and combinatorial nature of the problem, obtaining an optimal solution is challenging. To tackle this issue, we first develop an algorithm based on branch-and-bound (BB), whereby the feasible space is successively partitioned and searched by means of lower and upper bounds on the objective function. While this algorithm is shown to return an$\epsilon $-optimal solution within a finite number of iterations, it entails high computational complexity. Considering this limitation, we then reformulate the original problem into a more tractable form and conceive a low-complexity algorithm for its solution based on the penalty dual-decomposition technique. The proposed joint design algorithms for MIMO NOMA are evaluated by means of simulations over mmWave channels. Results show significant improvements in terms of total transmit power and spectral efficiency compared to benchmark approaches.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".