System Design and Throughput Analysis for Multihop Relaying in Cellular Systems
Bibliographic record
Abstract
Wireless multihop relaying can provide a great improvement in systemwide throughput and coverage in a cellular system. However, since additional resources (time or frequency slots) and overhead are required to perform relaying, the addition of relays into a cellular system must be carefully designed for the system's specific geometry, topology, and propagation environment. Maximizing the benefit of multihop relaying requires that the medium access control (MAC) layer scheduler be aware of physical (PHY) layer conditions. Multihop relaying increases networkwide throughput by enabling spatial reuse within a cell and by significantly reducing the path loss on each hop. The most significant gain results from adding enough relays to create line-of-sight (LOS) paths on all relay hops. To assist with system design, we present a methodology of analyzing network throughput and area-averaged spectral efficiency for multihop relay-enhanced cellular systems, including cross-layer considerations. In addition, we describe a technique that may be used to determine spatial reuse schedules. In this paper, we use a realistic model and typical cellular scenarios to show how relaying can be used to improve coverage and throughput in a real-world system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".