Bibliographic record
Abstract
In the past few decades, wireless communications have been growing rapidly and the growth rate is accelerating at a very fast pace.In order to meet this growing demand, researchers are struggling to solve two fundamental problems: (1) providing the spectrum resources needed to support clusters of dense users with very high data rate and (2) supplying the required spectrum at reasonable power requirement level.This thesis first outlines the extent of this capacity-power problem now and in the near future and discusses possible approaches to meet these difficult challenges.We start by introducing a new metric, called Total Energy Per Bit (TEPB), to be used as a measure for greening of the network.Then, a proposed vision of replacing Macro-eNBs with a large number of Pico-eNBs to bring the network closer to the users is provided.This naturally leads to casting the problem as an optimization problem with an objective function of minimizing the overall power consumption while supporting user demand with acceptable quality of service.Based on the optimization problem, we propose two algorithms based on the concept of self organizing network (SON) to switch on/off base stations.Simulation results show that both proposed switch on/off algorithms can improve the power requirement of the network while maintaining the required UE throughput by reducing the TEPB significantly.i Many people supported me during the completion of this thesis with criticism, helpful assistance and references.This thesis would have never been possible without them. I am greatly thankful to my supervisors Prof. Roshdy
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".