Achieving Optimal Throughput in Cooperative Wireless Multihop Networks With Rate Adaptation and Continuous Power Control
Bibliographic record
Abstract
This work is an offline study to characterize the performance of cooperative relaying in interference-limited multihop networks, where nodes are equipped with multi-rate and continuous power control capabilities. We formulate a cross-layer flow-based framework to obtain the achievable throughput rates by jointly optimizing the parameters for multi-path routing, scheduling, rates, transmit powers, and selection of cooperative nodes. This framework is generic in that it is not restricted to any particular cooperative combining technique or type of network architecture. To take continuous power control into account, we introduce a non-trivial power allocation subproblem while keeping the main cross-layer framework as a linear program. We solve the problem optimally to obtain the max-min throughput for the case when cooperation is based on the distributed Alamouti code and networks have a mesh-like topology. We derive a number of practical engineering insights based on our numerical optimal results obtained for small-to-medium-sized random networks. In particular, we establish that the use of cooperative relaying in a small-to-medium-sized random mesh network often does not yield significant performance gains in throughput and connectivity even when multi-rate and continuous power control capabilities are available at the nodes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".