Throughput and QoS optimization in nonuniform multichannel wireless mesh networks
Bibliographic record
Abstract
A technology to increase throughput and QoS in infrastructure-based Wireless Mesh Networks (WMNs) is proposed. In a uniform WMN, let each Base Station (BS) have R1 transceivers for communications with neighboring BSs, and R2 transceivers for communications with the Stationary and Mobile Subscribers within the wireless cell. One Gateway BS provides access to the global Internet, and the throughput capacity of the entire WMN is constrained by the IO bandwidth of the Gateway. A small number of extra wireless links can be added to the Gateway BS and selected other BSs, resulting in a nonuniform system. The addition of an asymptotically small number of transceivers can increase WMN capacity several fold. Efficient scheduling requires the partitioning of an asymmetric bipartite graph representing a general traffic rate matrix, into multiple graphs representing doubly-stochastic matrices. Routing and scheduling algorithms presented. The algorithms can provision long-term multimedia flows including VOIP or IPTV with guaranteed service. For multichannel WMNs where the traffic is routed and partitioned, the number of queued cells per BS is near-minimal and bounded, the end-to-end delay and jitter are near-minimal and bounded, and cell loss rates due to scheduling conflicts are zero. The algorithm also achieves 100% of capacity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".