Solving the frequency assignment problem with differential evolution

Maximiano, Marisa; Vega‐Rodríguez, Miguel A.; Gómez–Pulido, Juan A.; Sánchez‐Pérez, Juan M.

doi:10.1109/softcom.2007.4446075

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…Unique to other works, a realistic SIN R model is considered instead of simple binary interference, although SIN R is modeled and taken into account in the problem as a constraint, which is a completely different way from our work. Differential Evolution approaches include Da Silva Maximiano et al, who assign frequencies to base stations in Global System for Mobile Communications (GSM) using DE for minimising interference [50], [51]. Differential Evolution is also used for CA in DSA CR networks by Latif et al [52] and Anumandla et al [53].…”

Section: Related Work: Metaheuristic Algorithms For Ca In Dsa Wmnsmentioning

confidence: 99%

Comparison of Metaheuristic Algorithms for Interface-Constrained Channel Assignment in a Hybrid Dynamic Spectrum Access – Wi-Fi Infrastructure WMN

et al. 2022

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In this work, we evaluate the application of four different metaheuristic optimisation algorithms for solving the channel assignment problem in a multi-radio multi-channel Wireless Mesh Network (WMN) using Dynamic Spectrum Access (DSA). The work advances a near optimal channel assignment in a WMN that uses DSA by applying soft computing methods. While CA in a WMN is wellstudied, and CA for secondary user cognitive radio networks has also been studied in the literature, CA for our specific scenario of an infrastructure DSA-WMN is novel. This scenario poses new challenges because nodes are spread out geographically and so may have different allowed channels and experience different levels of external interference in different channels. A solution must meet two conflicting requirements simultaneously: 1) to avoid interference within the network and with external interference sources, and 2) maintain connectivity within the network; all while staying within the radio interface constraint, i.e., only assigning as many channels to a node as it has radios. Our method is unique in that it is protocol-agnostic, being able to avoid interference from external sources that use different protocols and standards. We present a novel algorithm, used alongside the metaheuristic optimisation algorithms, which ensures the feasibility of solutions in the search space. Average Signal to Interference and Noise Ratio (SIN R) over the network is used as the performance measure, with the goal of optimisation being to find the highest average SIN R. This is a more realistic performance measure than the binary on/off conflict-based measures most common in the literature. Our energy-based method also has the unique advantage that it is protocol-agnostic, being able to avoid interference from external sources that use different protocols and standards. The algorithms that are compared in this work are Simulated Annealing (SA), the Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Differential Evolution (DE). These algorithms were evaluated through the use of simulation in Network Simulator 3. Various parameters were experimented with for each of the employed algorithms. The resultant best set of parameters was used for the comparison of the four metaheuristic algorithms. It was found that the population-based algorithms (PSO, GA, and DE) all perform satisfactorily for this problem, although DE is superior to the others. SA can give acceptable solutions, but performs poorly in comparison to the population-based algorithms. The paper also considers the computational complexity of the methods. It is found that SA and DE perform well in this regard.

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Section: Related Work: Metaheuristic Algorithms For Ca In Dsa Wmnsmentioning

confidence: 99%

Comparison of Metaheuristic Algorithms for Interface-Constrained Channel Assignment in a Hybrid Dynamic Spectrum Access – Wi-Fi Infrastructure WMN

et al. 2022

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“…Assume there are N (N I − N ) omitted severe interfering neighboring cells in total, then the corresponding MMRs data R can be estimated by the regression model as R =C b (11) whereC is a matrix containing N Q rows of the augmented matrixC , and these N Q rows are related with the severe interfering neighboring cells which are omitted in the MMRs. The fused MMRs data are then constructed,…”

Section: Data Fusion and Im Generationmentioning

confidence: 99%

“…After applying Data Fusion I to Cell 1, three severe interfering neighboring cells omitted originally are recovered and their BS sites are marked as hollow five-pointed star in Figure 8. MMRs data of these three neighboring cells is calculated using (11) £and combined with the original MMRs data to form the reinforced MMRs data using (12). The obtained IM-MR' is surely different from the IM-MR obtained from the original data using traditional algorithms.…”

Section: Data Fusion and Obtained Imsmentioning

confidence: 99%

“…Frequency planning for large-scale cellular systems has been a hot research issue for decades. Early research focused on minimizing the number of frequencies needed while satisfying capacity demand and frequency separation constraint [1][2][3][4][5][6][7][8][9][10][11][12]. Here, frequency planning is formulated as an optimization problem, and the constraint of frequency separation between cell pairs is characterized by a compatibility matrix (also known as channel separation matrix) or exclusion matrix.…”

Section: Introductionmentioning

confidence: 99%

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Data fusion‐based interference matrix generation for cellular system frequency planning

Wu¹,

Huang²,

Zhou³

et al. 2011

Int J Communication

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SUMMARYInterference matrix (IM) has been widely used in frequency planning/optimization of cellular systems because it describes the interaction between any two cells. IM is generated from the source data gathered from the cellular system, either mobile measurement reports (MMRs) or drive test (DT) records. IM accuracy is not satisfactory since neither MMRs nor DT records contain complete information on interference and traffic distribution. In this paper, two IM generation algorithms based on source data fusion are proposed. Data fusion in one algorithm is to reinforce MMRs data, using the frequency-domain information of DT data from the same region. Data fusion in the other algorithm is to reshape DT data, using the traffic distribution information extracted from MMRs from the same region. The fused data contain more complete information so that more accurate IM can be obtained. Simulation results have validated this conclusion.

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Data Fusion Based Interference Matrix Generation for Cellular System Frequency Planning

Wu¹,

Huang²,

Zhou³

et al. 2010

Preprint

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Solving the frequency assignment problem with differential evolution

Cited by 3 publications

References 4 publications

Comparison of Metaheuristic Algorithms for Interface-Constrained Channel Assignment in a Hybrid Dynamic Spectrum Access – Wi-Fi Infrastructure WMN

Comparison of Metaheuristic Algorithms for Interface-Constrained Channel Assignment in a Hybrid Dynamic Spectrum Access – Wi-Fi Infrastructure WMN

Data fusion‐based interference matrix generation for cellular system frequency planning

Data Fusion Based Interference Matrix Generation for Cellular System Frequency Planning

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