Review of optical and wireless backhaul networks and emerging trends of next generation <scp>5G</scp> and <scp>6G</scp> technologies
Bibliographic record
Abstract
Abstract Mobile Backhauling provides an interface between radio controller and base stations, mostly realized with a physical medium such as optical fibers or microwave radio links. With the huge mobile traffic due to an increase in mobile subscribers as well as deployment of 4G and 5G cellular network technologies, better solutions for capacity and coverage should be provided in order to enhance spectral efficiency. For 4G cellular networks, mobile backhaul networks deal with capacity, availability, deployment cost, and long‐distance reaches. In addition, mobile backhaul networks based on the 5G network incurs additional challenges that include 1 ms or less ultralow latency time requirements and ultra‐dense nature of the network capabilities. Therefore, for 5G technologies, latency delay, QoS, packet efficiency, noise suppression, and mitigation techniques, efficient modulation schemes, and packet network timing synchronization are some aspects that are to be dealt with while designing efficient backhaul approaches (wired/wireless). Current backhaul systems typically use cost‐effective solutions (eg, ‐Wi‐Fi and WiMAX)‐based packet‐switched technologies, especially Ethernet/Internet technologies and high‐speed optical fiber links.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".