Cross-Layer Design and Performance Analysis of Tactical Radio Networks
Bibliographic record
Abstract
In this paper, we investigate the performance of tactical radio networks, particularly for communication scenarios where multihop relaying along with spatial reuse techniques are applied. The tactical scenarios of concern have a diverse range of reliability and/or delay requirements. We first employ cross-layer protocol architecture with integrated time-division-multiple-access-based fast packet forwarding and automatic-repeat-request-based multihop error control to support tactical applications with a diverse range of reliability and/or delay requirements. Then, we develop analytical models to study the link-level and network-wide behaviors of the tactical radio networks. The developed models capture the effects of end-to-end channel memory due to multihop relays, interference due to spatial reuse, uneven interslot delays due to fast packet forwarding, and jamming attack in hostile network environment. It is shown that the protocol architecture with proposed fast packet forwarding and multihop error control mechanisms can significantly improve communication performance and support delay-sensitive applications. In addition, it is demonstrated that cross-layer adaptivity, where different network environments require different logical topologies and radio modes, is needed to achieve best performance tradeoffs among throughput, efficiency, delivery ratio, and transport capacity.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".