QoS-aware resource provisioning in virtualized wireless networks
Bibliographic record
Abstract
Wireless virtualization is emerging as a promising paradigm to tackle the issues of spectrum-crisis and network ossification via enabling common shared substrate of wireless resources among service providers, commonly referred as slices.Due to random nature of wireless channels and limited resources, virtualized wireless network (VWN) requires an efficient resource provisioning policy to operate.The objective of this thesis is to study and propose quality-of-service-aware (QoS-aware) resource provisioning policies applicable to VWNs.The first part of the thesis focuses on the design of resource provisioning policies to satisfy instantaneous requirements (e.g., minimum reserved rates and resources) of slices and minimize the VWN outage due to infeasibility.At first, an optimal algorithm for resource provisioning is developed to simultaneously satisfy the rate and resource (power and sub-carriers) based requirements of two groups of slices.Afterwards, to deal with the issue of infeasibility in VWNs due to limited wireless resources, an admission control policy is proposed.In this context, an optimal admission control algorithm is developed to dynamically adjust the requirements of slices according to channel state information (CSI) and priorities of slices.Finally, to further improve the feasibility region, a joint power, sub-carrier and antenna allocation algorithm is developed for VWN with massive multiple input multiple output (MIMO) setup, where a base station (BS), equipped with large number of antennas, serves users belonging to different slices.The second part of the thesis focuses on more realistic design of VWNs.Specifically, the issues of random, bursty traffic arrival in users' queue, energy-efficiency and uncertain CSI at the BS, often experienced in practice, are addressed through resource provisioning policies.At first, to improve end-user service experience, cross-layer resource provisioning policies are presented.In this context, a dynamic resource provisioning policy, adaptable to both CSI and queue state information (QSI) of VWNs, is proposed to maintain the stable queue state of VWN.Afterwards, to minimize energy consumption, a resource provisioning policy is proposed to satisfy the maximum average packet transmission delay in VWNs to offer reliable end-users' experience.Finally, the issue of uncertain CSI at the BS due to estimation errors is addressed through an energy-efficient robust resource provisioning policy.The total energy consumption of VWN is easily controllable via the cost factors of slices in the proposed policy.Simulations are performed to deeply analyze the effects of system parameters on VWN's performance.me an opportunity to pursue research during my MEng.study at McGill University.Without his invaluable support and guidance, I could hardly imagine
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".