Fast Algorithm for Joint Unicast and Multicast Beamforming in Large-Scale Systems
Bibliographic record
Abstract
We consider a joint unicast and multi-group multicast beamforming design for a large-scale massive multiple-input multiple-output (MIMO) system, where there may be a large number of unicast users. We propose a fast algorithm that efficiently obtains the beamforming solutions for both unicast and multicast users to minimize the transmit power subject to quality-of-service (QoS) requirements. Utilizing the optimal beamforming structure obtained recently for multi-group multicast beamforming, we separate the original problem into two subproblems for the unicast and multicast users and solve them using the alternating optimization technique. We obtain the solution to the unicast subproblem in closed-form by exploring the unicast beamforming structure, which provides a key step in reducing the computational complexity of the overall algorithm. We solve the multicast subproblem using the successive convex optimization method whose complexity is independent of the number of unicast users and antennas. Simulation results show that our proposed algorithm achieves a near-optimal performance at a very low complexity for large-scale systems.
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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 | 0.000 | 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)
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".