Energy-Efficient User Clustering and Downlink Beamforming for MIMO-SCMA in C-RAN
Bibliographic record
Abstract
Multiple-inputmultiple-output (MIMO) sparse code multiple access (SCMA) is of great interest for future wireless networks to achieve higher spectral efficiency and support massive connectivity. In this paper, we investigate the key problems of user clustering and downlink beamforming for MIMO-SCMA in a cloud radio access network (C-RAN). Using channel state information available at the central processor, an efficient user clustering algorithm based on the constrained$K$-means method is proposed. Subsequently, two iterative algorithms for beamforming design are developed by minimizing the total transmission power under quality-of-service (QoS) and fronthaul capacity constraints. In the first approach, we approximate the continuous non-convex constraints by convex conic ones using first-order Taylor expansion and iteratively solve a sequence of mixed-integer second order cone programs (MI-SOCPs) to achieve high quality solution, but with higher complexity. In the second approach, a two-stage low-complexity solution is developed in which beamforming matrices obtained from each stage are combined to form a single beamformer for each user. In the first stage,clusterbeamformers are designed by taking advantage of block diagonalization, while in the second stage,user-specificbeamformers are determined by minimizing transmission power. The performance of the proposed user clustering and downlink beamforming approaches for MIMO-SCMA in C-RAN is validated through simulations over mmWave channels. Compared to benchmark approaches, the results show significant improvements in terms of transmit power and spectral efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".