QoS Provisioning Dynamic Connection-Admission Control for Multimedia Wireless Networks Using a Hopfield Neural Network

Ahn, Chang Wook; Ramakrishna, R. S.

doi:10.1109/tvt.2003.822000

Cited by 59 publications

(32 citation statements)

References 25 publications

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“…The VLSI implementation of the HNN has the capacity to find suboptimal solutions in a few microseconds [11], which is fast enough to establish a new resource allocation on a frame-by-frame basis in current wireless communication systems for relatively low mobile speeds (i.e. for flat-fading and slow-fading environments, which means the channel coherence time is much larger than the radio resource management algorithm's runtime).…”

Section: Introductionmentioning

confidence: 99%

Continuous-time Hopfield neural network-based optimized solution to 2-channel allocation problem

Uykan

2015

Turk J Elec Eng & Comp Sci

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Abstract:The channel allocation problem in cellular radio systems is NP-complete, and thus its general solution is not known for even the 2-channel case. It is well known that the link gain system matrix (or received-signal power system matrix) of the radio network is (and may be highly) asymmetric, and that as the Hopfield neural network is applied to optimization problems, its weight matrix should be symmetric. The main contribution of this paper is as follows: turning the channel allocation problem into a maxCut graph partitioning problem, we propose a simple and effective continuous-time Hopfield neural network-based solution by determining its symmetric weight matrix from the asymmetric received-signal-power-system matrix. Computer simulations confirm the effectiveness and superiority of the proposed solution as compared to standard algorithms for various illustrative cellular radio scenarios for the 2-channel case.

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Section: Introductionmentioning

confidence: 99%

Continuous-time Hopfield neural network-based optimized solution to 2-channel allocation problem

Uykan

2015

Turk J Elec Eng & Comp Sci

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“…As an alternative to deterministic scheduling [17] or genetic algorithms [28], neural networks have been used within the area of a multi-objective optimisation problems. In [2] authors provide dynamic admission control while preserving QoS by using hardware-based Hopefield neural networks [33].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Neuro–Wavelet Predictor for QoS Control and Stability

Napoli

Pappalardo

Tramontana

2013

AI*IA 2013: Advances in Artificial Intelligence

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“…Many types of algorithms have been proposed in the literature to solve optimization problems, such as genetic algorithms, game theory, linear programming or Hopfield Neural Networks (HNNs). Within this group, HNNs are identified as fast hardware optimizers that could obtain a solution in few microseconds [32]. This fast response is a consequence of the simplicity of each individual neuron and their parallel interworking.…”

Section: Jdra Algorithmmentioning

confidence: 99%

“…They proposed the usage of HNNs for the dynamic distribution of frequency channels over the cells of a GSM system together with a guard channel technique for handovers. Ahn and Ramakrishna [32] were the first authors to use HNNs for solving the DRA problem. In the main, their algorithm aimed at maximizing the allocated resources and obtaining a fair distribution among users.…”

Section: Jdra Algorithmmentioning

confidence: 99%

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Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks

Calabuig¹

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Nowadays several technologies coexist in the same area composing a heterogeneous system. Moreover, this fact is expected to become more and more accentuated with all new technologies that are currently being standardized. So far, generally users are who select the technology they are going to connect to, either configuring the user equipment or using different equipments. Nevertheless, this approach does not take the maximum advantage of the available resources. To this aim, a new set of strategies is necessary. These strategies must manage the radio resources of all technologies commonly and jointly, and they must ensure the satisfaction of users Quality of Service (QoS).Following this idea, this Thesis proposes two new algorithms. The first one is a Joint Dynamic Resource Allocation (JDRA) algorithm capable of allocating resources to users and distributing users among technologies at the same time. The algorithm is formulated as a multi-objective minimization problem that is solved using Hopfield Neural Networks (HNNs). HNNs are interesting because they are supposed to find suboptimal solutions in very short times. Nevertheless, actual implementations of HNNs in computers lose this fast response. For that reason, this Thesis analyses the causes and studies possible improvements.The second algorithm is a Joint Call Admission Control (JCAC) algorithm that admits and rejects users taking all technologies into account at the same time. The main difference with other proposed algorithms is that they decide on call admission in each technology separately. Thus, a previous mechanism is needed to select which technology users are going to connect to. On the other hand, the technique proposed in this Thesis is capable of deciding on call admission in the whole heterogeneous system. Therefore, users are not attached to any technology prior to admission.As a result, this Thesis paves the way to an efficient resource management in heterogeneous systems with new ideas and techniques. The functions of the Radio Resource Management (RRM) are divided into the JDRA and JCAC algorithms so they could work together. ResumenHoy en día existen varias tecnologías que coexisten en una misma zona formando un sistema heterogéneo. Además, este hecho se espera que se vuelva más acentuado con todas las nuevas tecnologías que se están estandarizando actualmente. Hasta ahora, generalmente son los usuarios los que eligen la tecnología a la que se van a conectar, ya sea configurando sus terminales o usando terminales distintos. Sin embargo, esta solución es incapaz de aprovechar al máximo todos los recursos. Para ello es necesario un nuevo conjunto de estrategias. Estas estrategias deben gestionar los recursos radio eléctricos conjuntamente y asegurar la satisfacción de la calidad de servicio de los usuarios.Siguiendo esta idea, esta Tesis propone dos nuevos algoritmos. El primero es un algoritmo de asignación dinámica de recursos conjunto (JDRA) capaz de asignar recursos a usuarios y de distribuir usuarios entre tecnologías al mismo ...

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QoS Provisioning Dynamic Connection-Admission Control for Multimedia Wireless Networks Using a Hopfield Neural Network

Cited by 59 publications

References 25 publications

Continuous-time Hopfield neural network-based optimized solution to 2-channel allocation problem

Continuous-time Hopfield neural network-based optimized solution to 2-channel allocation problem

A Hybrid Neuro–Wavelet Predictor for QoS Control and Stability

Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks

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