Delay-QoS Aware Adaptive Resource Allocations for Free Space Optical Fronthaul Networks
Bibliographic record
Abstract
Statistical delay quality-of-service (QoS) aware adaptive resource allocation scheme is proposed for a multi-carrier coherent free space optical (FSO) communications based fronthaul network. The proposed resource allocation assigns remote radio heads (RRHs) to the suitable aggregation nodes (ANs) and allocates the transmit power to the orthogonal optical carriers. Specifically, the proposed resource allocation provides delay-QoS at the link layer by maximizing the effective sum capacity of the all the RRHs subject to transmit power budgets at the RRHs and capacity constraint of the wired fronthaul links connecting the ANs with the baseband unit (BBU) pool. The considered resource allocation is formulated as a mixed-integer non-linear programing (MINLP) problem. We use two transmission link optimization techniques, namely, independent link optimization (ILO) and joint link optimization (JLO), in order to solve the proposed MINLP problem. Under both optimization techniques, the proposed MINLP problem is decomposed into two subproblems which are iteratively solved in order to obtain the optical transmit power allocation and assignments of RRHs to the ANs. Our analysis reveals that the optical transmit power allocation and RRH-AN assignments depend on both the atmospheric turbulence fading and delay-QoS requirements. Numerical results demonstrate that the JLO technique achieves significant higher effective capacity (EC) compared to the ILO technique in the strict statistical delay-QoS constraints. However, the EC performance gap between the JLO and ILO techniques is reduced in the loose statistical delay-QoS constraints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".