Achieving 5G NR mmWave Indoor Coverage Under Integrated Access Backhaul
Bibliographic record
Abstract
In this article, we introduce a novel millimeter wave (mmWave) access architecture, called mmWave over cable (mmWoC), for achieving effective indoor coverage under integrated access backhaul. The proposed mmWave access architecture is characterized by using an analog link over local area network cables to relay the frequency-converted signals between the mmWave small cell station at the outdoor and the mmWave antenna array in the indoor, so as to ensure line-of-sight transmissions for the indoor user equipment. We first develop a precoding process for the proposed mmWoC architecture where a cascaded radio–radio (R–R) mmWave channel (R–R channel) is seen at the mmWave base station. Then, rate analysis is provided for both the cascaded R–R channel and the cascaded radio–cable–radio channel (R–C–R channel). Furthermore, we investigate an efficient yet simple resource mapping scheme between the antenna signals and the cable subcarriers, namely nonconfigurable air-to-cable (NC-A2C), which ensures each cable subcarrier with sufficient capacity to meet the fifth-generation new radio (5G NR) requirement by manipulating the cable input powers. Simulation results indicate that the proposed NC-A2C scheduler can efficiently trade the capacity off for better control simplicity and cost effectiveness; and the proposed precoder can work well on the R–C–R indoor environment under the mmWoC architecture.
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) | 0.001 | 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".