Distributed Resource Allocation for Multihop Decode-and-Forward Relay Systems
Bibliographic record
Abstract
In multihop relay networks with a large number of hops, it is challenging to allocate the available resources optimally among the relay nodes due to the large number of required control signals between the controller and the relay nodes. In addition, the latency caused by this signaling can result in using outdated resource-allocation information as the channels are time varying. Hence, a distributed resource-allocation scheme is proposed for multihop decode-and-forward (DF) relay systems, with low complexity in analytical computation and practical implementation. Based on the type of division multiplexing in time or frequency, this scheme can be used to obtain the optimal per-hop time slot length or bandwidth, respectively. This distributed iterative scheme needs no additional resource for optimization. The required communication to fulfill this optimization is only with the adjacent nodes of each relay and can be performed through the forwarding and acknowledgment packets (ACKs). It is shown that this scheme converges to the global optimal solution with an exponential convergence rate. The performance of this scheme is also investigated for the time-variant transmission channels by computer simulations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".