Enhanced Fronthaul Capacity in CRANs: Sum-Rate Maximization via Joint Optimal Design of STAR-RIS, Massive MIMO and Data Compression
Bibliographic record
Abstract
Cloud Radio Access Networks (CRAN) face a critical challenge due to the limited capacity of fronthaul links overwhelmed by massive data transmissions. This paper proposes a novel CRAN design that effectively tackles this challenge. Our approach combines three key elements: (1) Massive MIMO at the baseband unit to leverage large array gain and interference suppression; (2) a novel simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) that can both transmit and reflect signals concurrently, improving fronthaul capacity through energy splitting technique by enabling communication with remote radio heads serving multiple user equipments; and (3) a data compression technique by optimizing the quantization noise covariance matrix across remote radio heads, significantly reducing the fronthaul traffic load. We formulate a problem to maximize the overall network sum-rate by jointly optimizing transmit power, fronthaul capacity, beamforming vectors at RRHs, data compression, and STAR-RIS transmission-reflection coefficients. To address the nonconvexity of the resulting joint optimization problem, successive convexification along with alternating optimization technique are used to develop an iterative algorithm. Simulations demonstrate that our STAR-RIS-aided CRAN design surpasses conventional reflecting-only RIS aided CRAN by providing full-space coverage and thus offering more degrees-of-freedom compared to traditional RIS.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".