A Radio Resource Management Framework for IEEE 802.16-Based OFDM/TDD Wireless Mesh Networks
Bibliographic record
Abstract
We present a radio resource management framework for the IEEE 802.16-based OFDM/TDD wireless mesh networks. The major components of the framework, namely, subchannel allocation, admission control, and route selection schemes are developed so that the quality of service (QoS) can be guaranteed on a per-connection basis. We formulate a tandem queueing model to obtain the in-connection performance measures such as average delay and packet data unit (PDU) dropping probability for relay connections in an end-to-end basis. The same formulation can be used to obtain the performance measures such as the average transmission delay for a PDU and the PDU dropping probability at a mesh-node (i.e., IEEE 802.16 base station) for local connections. Two subchannel allocation algorithms, namely, the optimal and the iterative subchannel algorithms, are proposed. The admission control and route selection schemes for relay connections are based on the performance measures obtained from the tandem queueing model. In particular, a new connection is accepted if the packet delay requirements corresponding to that connection can be satisfied and the route that provides the smallest delay is selected for routing packets through the network to the Internet gateway. The performances of the proposed radio resource management schemes are evaluated by simulations.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".