Low-Complexity Joint Antenna Selection and Robust Multi-Group Multicast Beamforming for Massive MIMO
Bibliographic record
Abstract
We consider low-complexity design for joint antenna selection and robust multi-group multicast beamforming in massive multiple-input multiple-output (MIMO) systems. Relying on the estimated channel covariance and assuming a limited number of antennas for transmission at the base station, we aim to minimize the transmit power subject to the worst-case signal-to-interference-plus-noise-ratio (SINR) guarantee and per selected antenna power budget. Converting the worst-case SINR constraints to a set of non-convex constraints, we propose a two-phase approach to solve the problem efficiently: the antenna selection phase, followed by the robust multicast beamforming generation phase. We propose an SINR-based approach for antenna selection, where the challenging mixed-integer problem is converted into an approximate joint optimization problem via a sequence of transformation, relaxation, and SINR approximation. We develop a fast two-layered alternating direction method of multipliers (ADMM)-based algorithm to compute an approximate solution. In particular, with our ADMM construction, we obtain semi-closed-form solutions for antenna selection and beamforming subproblems at each ADMM iteration for fast updates. To further reduce the computational complexity, we propose a signal-to-leakage-ratio (SLR)-based approach using the SLR constraints in the joint optimization problem. This allows us to develop a two-layered ADMM-based algorithm, which can compute a solution more efficiently due to the SLR structure. The robust multicast beamforming solution for the selected antennas is computed using the fast algorithm we developed recently. Simulation results show the effectiveness of our two proposed approximated approaches for antenna selection and the overall two-phase approach in both overall performance and substantially low computational complexity in a massive MIMO setting.
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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 | 0.000 |
| 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".