Models and algorithms for the design of third-generation mobile networks
Bibliographic record
Abstract
Building greenfield third generation (3G) mobile networks, expansion of existing 3G mobile networks, and integration of 3G mobile networks with 2G and/or other networks, requires extensive analysis and optimization related to WCDMA/CDMA 2000/TD-SCDMA network planning. 3G mobile networks require a new planning and optimization approach. An intuitive approach to resolving 3G related problems often leads to results inconsistent with reality, thus the efficient planning and optimization methods must be based on increasingly complex and sophisticated models. To address the cell planning problem for systems with WCDMA air interface, the following information are usually supposed to be known: (i) a set of candidate sites where BSs can be installed, (ii) a set of possible configurations of each base station (rotation, tilt, height), (iii) the traffic distribution estimated by using empirical prediction models, and (iv) the propagation description based on approximate radio channel models or ray tracing techniques. To take into account SIR constraints, two power control (PC) models are considered: a power-based PC model which assumes that emission powers are adjusted to guarantee a target received power and a SIR-based PC model that assumes that emission powers are adjusted to guarantee a SIR target value to all active links. This thesis presents optimization models for radio network planning in 3G downlink. The models concern optimization of the base station parameters such as base station location, maximum transmit power and antenna height, all of which can be individually assigned to each base station. All these choices must satisfy a set of constraints and optimize a set of objectives. We develop heuristic and stochastic optimization techniques for tackling this highly combinatorial problem. In this downlink work, an extensive experimental analysis is presented to compare the effects on the radio network capacity, coverage, and investment requirements between SIR-based power control mechanism and power-based power control mechanism, between uplink and downlink environment, and between different network elements cost configurations. This thesis also uses tabu search to solve the 3G uplink cell planning problem as a discrete optimization problem, where base station location and configuration are basic decision variables. (Abstract shortened by UMI.)
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".