Energy-Efficient Group-Sparse Transceiver Design for Multiuser MIMO Relaying in C-RAN
Bibliographic record
Abstract
This paper addresses the problem of centralized transceiver design for multiuser MIMO amplify-and-forward (AF) relaying within a cloud radio access network (C-RAN). The aim is to optimize AF matrices of remote radio heads (RRHs) acting as relays, in order to improve the reception quality at the destinations while reducing network power consumption and feedback overhead on the fronthaul links. A two-stage method is proposed to solve this problem efficiently. The first stage relies on interference leakage minimization subject to per-relay transmit power constraints along with signal preserving constraints. To reduce the total network power, RRH selection is achieved by incorporating in the objective function a regularization term that promotes group-sparsity among the RRHs. In the second stage, to reduce feedback overhead, a different penalty term is added that induces weight-level sparsity in the AF matrix of each active RRH. For both stages, low-complexity iterative algorithms based on the alternating direction method of multipliers (ADMM) are developed to solve the corresponding regularized problems with low complexity. Extensive simulations are performed to demonstrate the explicit benefits of the proposed design method, which results in notably lower power consumption, computational complexity and weight feedback overhead than conventional 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 | 0.001 | 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 | 0.000 |
| 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".