Overlaying 5G radio access networks on wavelength division multiplexed optical access networks with carrier distribution
Bibliographic record
Abstract
As 5G communication matures, the requirement for advanced radio access networks (RAN) drives the evolution of optical access networks to support these needs. Basic RAN functions, mobile front-haul to the backbone and interconnected front-end remote radio units, must support and enable data rate surges, low-latency applications, RF coordination, etc. Wavelength division multiplexed optical access networks (WDM-OANs) provide sufficient network capacity to support the addition of RAN services, especially in unused portions of WDM. We propose and demonstrate a method for RAN overlay in WDM-OANs that employ distributed carriers. In such systems, the carrier is modulated at the central office for direct-detected downstream digital data services; later the same carrier is remodulated for the uplink. We propose the use of silicon photonics to intercept the downstream and add 5G signals. We examine the distributed-carrier power budget issues in this overlay scenario. The carrier power must be harvested for direct detection of both digital and RoF services, and yet hold in reserve sufficient power for the uplink remodulation of all services. We concentrate on the silicon photonics subsystem at the remote node to add RoF signals. We demonstrate the overlay with a fabricated chip and study strategic allocations of carrier power at the optical network units housing the radio units to support the overlay. After the successful drop and reception of both conventional WDM-OAN and the newly overlaid RoF signals, we demonstrate sufficient carrier power margin for the upstream remodulation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".