A simulation-based algorithm for solving the resource-assignment problem in satellite telecommunication networks

This paper proposes an heuristic for the scheduling of capacity requests and the periodic assignment of radio resources in geostationary (GEO) satellite networks with star topology, using the Demand Assigned Multiple Access (DAMA) protocol in the link layer, and Multi-Frequency Time Division Multiple Access (MF-TDMA) and Adaptive Coding and Modulation (ACM) in the physical layer.En este trabajo se propone una heurística para la programación de las solicitudes de capacidad y la asignación periódica de los recursos de radio en las redes de satélites geoestacionarios (GEO) con topología en estrella, con la demanda de acceso múltiple de asignación (DAMA) de protocolo en la capa de enlace, y el Multi-Frequency Time Division (Acceso múltiple por MF-TDMA) y codificación y modulación Adaptable (ACM) en la capa física.En aquest treball es proposa una heurística per a la programació de les sol·licituds de capacitat i l'assignació periòdica dels recursos de ràdio en les xarxes de satèl·lits geoestacionaris (GEO) amb topologia en estrella, amb la demanda d'accés múltiple d'assignació (DAMA) de protocol en la capa d'enllaç, i el Multi-Frequency Time Division (Accés múltiple per MF-TDMA) i codificació i modulació Adaptable (ACM) a la capa física

A parallel evolutionary algorithm applied to the minimum interference frequency assignment problem

This article presents the application of a Parallel Evolutionary Algorithm to solve the Minimum Interference Frequency Assignment Problem (MI-FAP). This is a capital problem in the mobile telecommunication field, which proposes to find an assignation of a set of frequencies to minimize the communication interference. MI-FAP is a NP-Complete optimization problem; so traditional exact algorithms are useless for solving real-life problem instances in reasonable execution times. This work proposes to use a metaheuristic approach to find good quality solutions for real-life MIFAP instances never faced before using Evolutionary Algorithms. Evaluation experiments performed on those real-life instances report promising numerical results for both serial and parallel models of the algorithm proposed. In addition, the parallel version shows high levels of computational efficiency, demonstrating a superlinear speedup behavior for the instances studiedVII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Heuristic Approaches to Solve the Frequency Assignment Problem

The frequency assignment problem is a computationally hard problem with many applications including the mobile telephone industry and tactical communications. The problem may be modelled mathematically as a T-colouring problem for an undirected weighted graph; it is required to assign to each vertex a value from a given set such that for each edge the difference in absolute value between the values at the corresponding vertices is greater than or equal to the weight of the edge. This problem was solved using novel and existing metaheuristic algorithms and their relative successes were compared. Early work of this thesis used greedy, steepest descent and backtracking algorithms as a means of investigating the factors which influence the performance of an algorithm (selection of frequency, ordering of variables, provision of an incremental objective function). Later simulated annealing, tabu search and divide and conquer techniques were used and the results compared. A novel divide and conquer technique incorporating metaheuristics is described and results using test data based on real problems is presented. The divide and conquer technique (with either tabu search or simulated annealing) was found to improve significantly upon the corresponding metaheuristic when implemented alone and acting on non-trivial scenarios. The results were significant and consistent. The divide and conquer (with simulated annealing) algorithm in particular was shown to be robust and efficient in its solution of the frequency assignment problems presented. The results presented in this thesis consistently out-perform those obtained by the Defence, Evaluation and Research Agency, Malvern. In addition this method lends itself to parallelisation since the problem is broken into smaller independent parts. The divide and conquer algorithm does not exploit knowledge of the constraint network and should be applicable to a number of different problem domains. Algorithms capable of solving the frequency assignment problem most effectively will become valuable as demand for the electromagnetic spectrum continues to grow

The Radio Number of Grid Graphs

The radio number problem uses a graph-theoretical model to simulate optimal frequency assignments on wireless networks. A radio labeling of a connected graph $G$ is a function $f:V(G) \to \mathbb Z_{0}^+$ such that for every pair of vertices $u,v \in V(G)$, we have $\lvert f(u)-f(v)\rvert \ge \text{diam}(G) + 1 - d(u,v)$ where $\text{diam}(G)$ denotes the diameter of $G$ and $d(u,v)$ the distance between vertices $u$ and $v$. Let $\text{span}(f)$ be the difference between the greatest label and least label assigned to $V(G)$. Then, the \textit{radio number} of a graph $\text{rn}(G)$ is defined as the minimum value of $\text{span}(f)$ over all radio labelings of $G$. So far, there have been few results on the radio number of the grid graph: In 2009 Calles and Gomez gave an upper and lower bound for square grids, and in 2008 Flores and Lewis were unable to completely determine the radio number of the ladder graph (a 2 by $n$ grid). In this paper, we completely determine the radio number of the grid graph $G_{a,b}$ for $a,b>2$, characterizing three subcases of the problem and providing a closed-form solution to each. These results have implications in the optimization of radio frequency assignment in wireless networks such as cell towers and environmental sensors.Comment: 17 pages, 7 figure