Feedback Energy Reduction in Massive MIMO Systems
Bibliographic record
Abstract
The availability of channel state information (CSI) at the transmitter plays a central role to provide high system performance in massive multiple-input multiple-output (MIMO) systems. In a frequency division duplexing (FDD) system, acquiring this information requires a prohibitive amount of feedback and a significant feedback energy, since it increases with the number of transmit antenna. In this paper, we address the issue of significant energy consumed to feedback all CSI to the base station (BS). To this end, we propose a novel feedback routing scheme based on transmit antenna selection for massive MIMO systems. The proposed scheme aims to jointly reduce the energy needed to feedback the CSI to the BS and the complexity of the transmit antenna selection. We formulate the problem of finding the feedback routing that minimizes the energy consumption as a least cost Hamiltonian path problem. To solve the formulated problem, we propose both an integer linear programming formulation to find the optimal solution and a heuristic dynamic programming algorithm to find a suboptimal solution with reasonable computational complexity. Computer simulations show that our scheme offers enormous reduction in feedback energy while ensuring low computational complexity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".