Dynamic SON-Enabled Location Management in LTE Networks
Bibliographic record
Abstract
Wireless networks are facing various challenges that demand continuous and rapid improvement. Long-Term Evolution (LTE) is a preferred wireless technology because of its satisfactory performance. Owing to an exponential increase in demand and new potential applications, the core network of LTE, which is known as the Evolved Packet Core (EPC), is affected by a surge in signaling caused by a variety of control functions. The signaling overhead decreases the users' Quality of Experience (QoE). The current study attempts to improve the intelligence of location management techniques. As an extension of our previous study [1], a Self-Organizing Network (SON) that enables dynamic reconfiguration of cell-to-TAL/MME is introduced. Both centralized and distributed pooling schemes are tested in terms of signaling overhead and user power consumption. A decomposition model that reduces the original formulated problem to two sub-problems is proposed, each of which is solved optimally. In addition, a smart cell-to-TAL selection scheme is proposed to prioritize potential cells that might be visited by a user equipment (UE). Our method is shown to outperform several state-of-the-art methods presented in the literature. Finally, a heuristic algorithm is presented to obtain a less complex solution than the optimal one.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".