Downlink Optimization in Cloud Radio Access Networks with Hybrid RF/FSO Fronthaul
Bibliographic record
Abstract
This paper studies the downlink of a cloud radio access network (C-RAN) that incorporates a baseband central processor (CP), multiple remote radio units (RUs), and a network of wireless fronthaul links that connect the RUs to the CP. The fronthaul network utilizes dedicated point-to-point free-space optical (FSO) links along with a broadcast radio frequency (RF) channel. The spectrum of the RF channel is also utilized for downlink transmission from the RUs to the mobile users. That is, the available RF spectrum is time-shared (in a half-duplex manner) among the fronthaul and downlink. The data symbols intended for different users are linearly precoded at the CP in the form of quantized in-phase and quadrature (IQ) samples. These samples are compressed then delivered via the fronthaul network to the corresponding RUs. The RUs, in turn, perform decompression and IQ modulation, before broadcasting their RF signals to the users. We focus on the joint design of the linear precoders, quantizers, and capacity of the RF fronthaul links, along with the time allocation of the RF spectrum, in order to maximize the weighted sum-rate of the users, subject to power constraints and capacity limitations of the hybrid fronthaul network. The resulting problem is nonconvex and difficult to handle. Therefore, we propose a computationally-tractable algorithm that utilizes line search and alternating convex optimization in order to obtain a high-quality suboptimal solution. We provide numerical examples to demonstrate the performance of the proposed algorithm under different weather conditions. We also show the performance gain of hybrid RF/FSO fronthaul, as compared to FSO-only fronthaul, during unfavorable weather conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".