Spectrally Efficient Frequency Reuse With Higher Order Sectorisation and Directional Relays in a Two-Hop Relay Network
Bibliographic record
Abstract
Two-hop relay networks for critical industries require high reliability within a limited spectrum. The lower hop between densely populated user equipment (UE) and the relay nodes (RNs) is assumed to have low spectral efficiency due to the inexpensive low-powered UEs equipped with omnidirectional antennas in non-line-of-sight (NLOS) conditions. Thus, spectrally efficient frequency reuse (FR) in the upper hop between RNs and base stations (BSs) is pivotal in enhancing the two-hop relay network’s uplink coverage and capacity. To achieve this objective, we propose the deployment of higher order sectorisation (HOS) at BS and directional antennas (DAs) at RNs in the upper hop and propose rotational (RFR), double (DFR), and triple (TFR) frequency reuse schemes. These schemes allow FR by exploiting the orientation of the DAs at RNs and are compared with conventional distance-based FR. Furthermore, an optimisation method is formulated to partition the limited bandwidth between the two hops to increase the average end-to-end uplink capacity while using the proposed FR schemes on the upper hop. When evaluating system performance with HOS at BSs (upper hop) and RNs (lower hop) in irregular cellular layout, the statistical evaluation of uplink interference becomes computationally expensive. To tackle this issue, we developed a semi-analytical model to efficiently compute the statistical distribution of uplink interference with reduced time and complexity. It is shown that this model is an accurate tool for performance assessments, which also validate the end-to-end two-hop capacity improvements up to 73% and 110% using the proposed DFR and TFR schemes, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".