Channel assignment for cellular radio using extended dynamic programming

Shirazi, Alireza Ghassempour; Amindavar, Hamidreza

doi:10.1016/j.aeue.2004.11.036

Cited by 8 publications

(3 citation statements)

References 14 publications

(37 reference statements)

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“…The problem of frequency distribution between wireless radio stations operating in a high-and a low-frequency band has been addressed using a model of the electromagnetic compatibility problem [109]. An effective solution was thus obtained faster than by the conventional backtracking method.…”

Section: Trajectory Methodsmentioning

confidence: 99%

Towards Optimal Frequency Plans: A Survey of Frequency Assignment Strategies, Models, and Methods

Mahmoudi,

Benatchba

et al. 2023

ENPESJ

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The frequency assignment problem in a telecommunication network refers to the task of assigning frequencies to transmitters to minimize or eliminate interference and to optimize at least one other parameter. The literature on this task is vast and examines many parameter optimization objectives. Numerous schemes, models, and methods have been proposed. In this article, we present an extensive survey of the published studies on the frequency assignment problem in the context of a wide range of applications. Different types of optimization objectives, models and solution methods are presented in a systematic way.

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Section: Trajectory Methodsmentioning

confidence: 99%

Towards Optimal Frequency Plans: A Survey of Frequency Assignment Strategies, Models, and Methods

Mahmoudi,

Benatchba

et al. 2023

ENPESJ

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“…Also, cost includes memory and energy consumption due to caching at the small BS. Two cases are examined where in the first case, costs and content popularity follow known and stationary distributions, formulating a dynamic programming problem [52] that is solved through value-iteration-based RL. The second and more practical case considers limited cache capacity and unknown cost distributions and employs an online low-complexity Q-learning solver to determine the content update strategy.…”

Section: ) Energy Efficiencymentioning

confidence: 99%

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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Mobile networks experience a tremendous increase in data volume and user density due to the massive number of coexisting users and devices. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting cache-aided edge nodes, such as fixed and mobile access points, and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers new opportunities for network optimization when traditional optimization approaches fail or incur high complexity. Among the various machine learning categories, reinforcement learning provides autonomous operation without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching solutions are presented and classified, based on the networking architecture and optimization target. As sixth generation (6G) networks will be characterized by high heterogeneity, fixed cellular, fog, cooperative, vehicular, and aerial networks are studied. The discussion of these works reveals that there exist reinforcement learning-aided caching schemes with varying complexity that can surpass the performance of conventional policy-based approaches. Finally, several open issues are presented, stimulating further interest in this important research field.INDEX TERMS 6G, edge caching, heterogeneous networks, machine learning, mobile edge networks, reinforcement learning.

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“…Por ejemplo, el método de mutación inversa [34], selec- ciona dos posiciones al azar de un cromosoma invirtiendo la sub-cadena entre las posiciones, tal y como se muestra en la figura 2.4a. Los resultados de la aplicación del método de mutación inversiva tienen una gran variedad entre los autores de CAP, mientras [35,36] afirman que el resultado tiene una incidencia enorme en la calidad de la aptitud de los individuos, [37,38] afirman que tiene una baja incidencia sobre los individuos mutados. En [23] utilizan ejecutan una mutación por inserción, donde se selecciona un gen para insertarlo en una posición aleatoria, como lo muestra la figura 2.4b.…”

Section: Operador De Mutaciónunclassified

Estrategias de optimización con algoritmos genéticos complejos en la resolución de problemas de asignación de canales en redes celulares

Torres¹

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En esta investigación se presentan los resultados comparativos de la implementación de algoritmos genéticos para la solución del problema de asignación de canales fijos en el contexto de comunicaciones móviles celulares. Se evalúa la convergencia y eficiencia de algoritmos genéticos paralelo con 6 islas interconectadas según las topologías anillo, estrella y malla, inspiradas en los postulados de la teoría de "Evolución de las especies por selección natural", para los Benchmarking de Philadelphia. Adicionalmente se presenta la comparación de resultados entre el algoritmo F-DR, algoritmo simple y paralelos, teniendo en cuenta una distribución genes dominantes.

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Channel assignment for cellular radio using extended dynamic programming

Cited by 8 publications

References 14 publications

Towards Optimal Frequency Plans: A Survey of Frequency Assignment Strategies, Models, and Methods

Towards Optimal Frequency Plans: A Survey of Frequency Assignment Strategies, Models, and Methods

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

Estrategias de optimización con algoritmos genéticos complejos en la resolución de problemas de asignación de canales en redes celulares

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