Broadband wireless network planning using evolutionary algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we present a simultaneous planning of Base Stations (BSs) and Relay Stations (RSs) with link flow for a broadband wireless network. Infrastructure costs (BS cost, RS cost and their operational costs) of a wireless network is a key factor for network service providers while planning a network. The objective of this problem is to determine a set of BSs and RSs that can serve all users and fulfill their demands at the lowest cost. This problem settings is equally important for planning networks from scratch or enhancements in existing networks. This combinatorial optimization problem is NP-hard in nature. Evolutionary Algorithms (EAs) are intelligent tools that can provide high quality solution to this type of problems. Usually, efficiency of EAs depends on the problem. The aim is to find effective EAs with minimum resources such as low computational complexity, processing time and number of fitness functions evaluations. We formulate this problem as a non-linear discrete optimization and introduce four recent EAs that are motivated by natural intelligent behaviors. The objective function of this planning problem is computationally costly, and there exist a tradeoff between resources and quality of solution. These algorithms include Biogeography-based Optimization (BBO) that is inspired by the natural migration phenomenon of species between different islands, Artificial Bee Colony (ABC) based on the intelligent behavior of honey bee swarms, Quantum-inspired Evolutionary Algorithm (QEA) from the idea of quantum computing, and Immune Quantum Evolutionary Algorithm (IQEA) motivated by both the immune theory and quantum computing. Simulation results demonstrate insights of EAs' and present tradeoff between resources and quality of solutions.
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.
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 it