Resource Allocation in C-RAN with Hybrid RF/FSO and Full-duplex Self-Backhauling Radio Units
Bibliographic record
Abstract
This paper considers the downlink of a cloud radio access network (C-RAN) consisting of a central processor (CP) and a network of connected radio units (RUs). We propose a novel resource allocation solution for the scenario with full-duplex (FD) self-backhauling RUs connected through hybrid radio-frequency (RF)/free-space optical (FSO) links to the CP for improved network throughput. This enables us to study the feasibility of the FD mode in terms of required self-interference cancellation to outperform the benchmark half-duplex hybrid RF/FSO transmission. Since the derived optimization problem for the design of the linear precoders and quantizers subject to the fronthaul capacity, zero-forcing, and power constraints, is non-convex and intractable, we develop an algorithm to solve it via an alternating optimization approach. In the simulation results, the proposed hybrid RF/FSO policy is assessed in terms of achievable rate, and we highlight the parameter range for which FD transmission is more rewarding than the time-division approach, under different weather conditions and selected RF bandwidth.
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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 | 0.000 |
| 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 | 0.000 |
| 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".