Analysis of Common Radio Resource Management Scheme for End-to-End QoS Support in Multiservice Heterogeneous Wireless Networks
Bibliographic record
Abstract
Future-generation wireless networks will consist of heterogeneous radio access technologies (RATs) with Internet Protocol-based infrastructure and support multiple bearer services having different quality-of-service (QoS) requirements. However, a major issue is how to jointly utilize the resources in the different RATs in an efficient manner while simultaneously achieving the desired QoS and minimizing the service cost from both the user and service provider perspectives. To resolve this issue, this paper proposes an adaptive common radio resource management (CRRM) scheme in the context of CDMA2000 and IEEE 802.11 wireless technologies, which are examples of today's wireless wide-area network (WWAN) and wireless local area network (WLAN) RATs, respectively. The key parameters of the scheme, i.e., service type, user mobility and location information, and service cost, are described. The effectiveness of the proposed CRRM scheme is analytically assessed using the theory of Markov chains. Numerical results show that the proposed CRRM scheme minimizes the rate of unnecessary vertical handoffs, thereby providing stable communication without degrading call-blocking probabilities in all mobility and loading scenarios considered. The proposed CRRM scheme also minimizes service cost, which makes it attractive for implementation in heterogeneous wireless networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".