Multi-Hop Capacity of MIMO-Multiplexing Relaying in WiMAX Mesh Networks
Bibliographic record
Abstract
One of the main challenges for metro-scale WiMAX mesh network deployments is related to capacity scaling. In a full mesh mode, a WiMAX node acts as a mesh router as well as a client access node. To improve latency and speed performance, typical dual- and multi-radio mesh solutions use different radio channels to create separate links for access and mesh relaying services. The available spectrum is therefore split between mesh and client access services. Network operators operating the WiMAX system over the licensed spectrum are not keen to provide separate radio channels for access and mesh relay services, as this reduces the total number of users serviced per spectrum allocation. MIMO-multiplexing relaying approach however provides separate links for access and mesh relaying services on the same radio channel. In this paper, we discuss the multi-hop capacity of OFDM-based MIMO-multiplexing relaying in WiMAX networks. For an NxN MIMO-multiplexing relaying with amplification factor alpha at relay nodes, R-hops relaying degrade the capacity by at most -Nlog2(alpha2R/(1 +SigmaRr-1alpha2rNr)) +RN/log2(N) bits/sec/Hz. Therefore, greater capacity loss is experienced in networks employing high-order MIMO- multiplexing relaying. We also show that the capacity loss is independent of the OFDM configurations employed; thus network operators could employ higher OFDM configurations to compensate data rate loss in access services when some of the MIMO-multiplexing links are dedicated to mesh relay. This analysis provides useful guidelines for operators planning MIMO-multiplexing option for mesh support in WiMAX network.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".