A Hybrid Hopfield Network-Simulated Annealing Approach for Frequency Assignment in Satellite Communications Systems

Salcedo‐Sanz, Sancho; Santiago-Mozos, Ricardo; Bousoño-Calzón, Carlos

doi:10.1109/tsmcb.2003.821458

Cited by 63 publications

(45 citation statements)

References 15 publications

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“…It is worth noting that most optimization strategies considered in the available literature dedicated to resource allocation make use of genetic algorithms or neural networks. In [15] the Simulated Annealing (SA) algorithm [16], has been proposed to minimize the co-channel interference in the uplink of two independent satellite systems. In [17] a reinforcement learning approach against satellite channel impairments such as orbital perturbations and atmospheric effects is presented.…”

Section: Introductionmentioning

confidence: 99%

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Cocco

Cola

Angelone

et al. 2018

IEEE Trans. on Broadcast.

134

147

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The increasing demand for high-rate broadcast and multicast services over satellite networks has pushed for the development of High Throughput Satellites characterized by a large number of beams (e.g., more than 100). This, together with the variable distribution of data traffic request across beams and over time, has called for the design of a new generation of satellite payloads, able to flexibly allocate bandwidth and power. In this context, this paper studies the problem of radio resource allocation in the forward link of multibeam satellite networks adopting the Digital Video Broadcasting -Satellite -Second Generation (DVB-S2) standard. We propose a novel objective function with the aim to meet as close as possible the requested traffic across the beams while taking fairness into account. The resulting non-convex optimization problem is solved using a modified version of the simulated annealing algorithm, for which a detailed complexity analysis is presented. Simulation results obtained under realistic conditions confirm the effectiveness of the proposed approach and shed some light on possible payload design implications.

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Section: Introductionmentioning

confidence: 99%

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Cocco

Cola

Angelone

et al. 2018

IEEE Trans. on Broadcast.

134

147

View full text Add to dashboard Cite

show abstract

“…There have been similar previous approaches to other optimization problems using a hybrid model Hopfield Network-Simulated Annealing as the works by Macías et al [5] and Salcedo-Sanz et al [22], SA hybridized with other optimization procedures as the works by Kim et al [13] and Yao [28].…”

Section: Hybrid Approach Gahnnmentioning

confidence: 96%

Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions

Salcedo‐Sanz

Yao

2005

Genet Program Evolvable Mach

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Abstract. In this paper we present three meta-heuristic approaches for FPGA segmented channel routing problems (FSCRPs) with a new cost function in which the cost of each assignment is not known in advance, and the cost of a solution only can be obtained from entire feasible assignments. Previous approaches to FSCPs cannot be applied to this kind of cost functions, and meta-heuristics are a good option to tackle the problem. We present two hybrid algorithms which use a Hopfield neural network to solve the problem's constraints, mixed with a Genetic Algorithm (GA) and a Simulated Annealing (SA). The third approach is a GA which manages the problem's constraints with a penalty function. We provide a complete analysis of the three metaheuristics, by tested them in several FSCRP instances, and comparing their performance and suitability to solve the FSCRP.

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“…Note that the updating rule only takes into account neurons x pq with value 1 and within a distance of c ip in columns of the element x ik being updated [12,13]. The matrixĈ is an n Â n matrix which encodes the problem's constraints given by Eq.…”

Section: The Hopfield Neural Networkmentioning

confidence: 99%

“…In a cycle, every neuron is updated once following the given order p(i), which is fixed during the execution of the algorithm [13,14]. After every cycle, the convergence of the HNN is checked.…”

Section: The Hopfield Neural Networkmentioning

confidence: 99%

Assignment of cells to switches in a cellular mobile network using a hybrid Hopfield network-genetic algorithm approach

Salcedo‐Sanz

Yao

2008

Applied Soft Computing

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Handoff and cabling cost management plays a key role in the design of cellular telecommunications networks. The efficient assignment of cells to switches in this type of networks is an NP-complete problem which cannot be solved efficiently unless P = NP. This paper presents a hybrid Hopfield network-genetic algorithm approach to the cell-to-switches assignment problem, in which a Hopfield network manages the problem's constraints, and a genetic algorithm searches for high quality solutions with the minimum possible cost in terms of handoff and cable displayed. We show, by means of computational experiments, the good performance of our approach to this problem. #

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A Hybrid Hopfield Network-Simulated Annealing Approach for Frequency Assignment in Satellite Communications Systems

Cited by 63 publications

References 15 publications

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions

Assignment of cells to switches in a cellular mobile network using a hybrid Hopfield network-genetic algorithm approach

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