Bibliographic record
Abstract
Long Term Evolution-Advanced (LTE-A) is a novel mobile network standard that can address a number of challenges that network operators face while trying to support the growing demand for high data rates.To do so, LTE introduces a number of technologies including Coordinated MultiPoint (CoMP) and Device-to-Device (D2D) communication.The performance of proposed protocols needs to be evaluated before the protocols are deployed on live networks.In fact, Modeling and Simulation (M&S) plays an important role in the development of modern cellular networks since it allows researchers to gain an insight into the operation of the networks in a cost and time effective manner.Discrete EVent System Specification (DEVS) provides a formal platform for M&S of discrete event dynamic systems.In this thesis, we present a general DEVS-based model for LTE networks.The model implements the basic functionality of each of the layers of the LTE protocol stack.In addition, it was designed to be flexible and modular.The model can be easily adapted to model various network deployments and scenarios, communication protocols, and propagation models.Moreover, in this thesis, we present two novel algorithms that aim to improve the upload performance of UEs in LTE networks.The two algorithms, Shared Segmented Upload (SSU) and Upload User Collaboration (UUC), were developed in collaboration with fellow students at Carleton University, and Ericsson Canada.The algorithms rely on some of the technologies of LTE to enhance the upload process for UEs in the network, especially when the UEs are located at or near the edges of their cells.The DEVS-based model we developed was used to conduct a series of system-level simulations to test the performance of the proposed algorithms, and compare them against the performance of traditional methods.The simulation results show that compared to the conventional methods, SSU improves the uplink performance for cell-edge UEs.In addition, UUC was shown to provide significant performance improvements for UEs regardless of their location within their cells, and can be applied to situations where CoMP is not available.xii
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".