QoS-Aware Frequency-Space Network Slicing and Admission Control for Virtual Wireless Networks
Bibliographic record
Abstract
Wireless virtualization is a promising approach to foster innovation and prevent the ossification of wireless networks. Within a virtualized wireless network, multiple network slices, or virtual operators (VO), are co- hosted on the same physical infrastructure. A fundamental question in this environment is which multiplexing technique, TDMA, FDMA or SDMA, should be used to slice the network among the VOs. Another related question is how should the stochastic arrival process affect the slicing and QoS criteria. To answer these two questions, we study the problem of QoS-aware joint admission control and network slicing. Due to the NP- hardness of the problem, we approach it using a heuristic algorithm composed of three steps: spectrum allocation, admission control and spatial multiplexing. The proposed algorithm incorporates the effects of QoS and stochastic traffic. We study through simulations the benefits of joint spatial- frequency multiplexing over the static frequency slicing approach. Finally, our simulation results help shed some light on the trade-offs between frequency and spatial multiplexing as well as between QoS and utilization.
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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) | 0.001 | 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 | 0.001 | 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".