Packet transmission on the WCDMA air interface using a rate matched bandwidth allocation technique

Wireless Mesh Network (MWN) could be divided into proactive routing, reactive routing and hybrid routing, which must satisfy the requirements related to scalability, reliability, flexibility, throughput, load balancing, congestion control and efficiency. DMN (Directional Mesh Network) become more adaptive to the local environments and robust to spectrum changes. The existing computing units in the mesh network systems are Fog nodes, the DMN architecture is more economic and efficient since it doesn’t require architecture- level changes from existing systems. The cluster head (CH) manages a group of nodes such that the network has the hierarchical structure for the channel access, routing and bandwidth allocation. The feature extraction and situational awareness is conducted, each Fog node sends the information regarding the current situation to the cluster head in the contextual format. A Markov logic network (MLN) based reasoning engine is utilized for the final routing table updating regarding the system uncertainty and complexity.

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An Online Bandwidth Allocation Scheme Based on Mechanism Design Model

IEICE Transactions on Communications ◽

10.1587/transcom.e96.b.321 ◽

2013 ◽

Vol E96.B (1) ◽

pp. 321-324 ◽

Cited By ~ 1

Author(s):

Sungwook KIM

Keyword(s):

Mechanism Design ◽

Bandwidth Allocation ◽

Design Model ◽

Allocation Scheme

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FRACTALIZATION OF CONVECTIVE FLOW UPON VERTICAL GROOVED DISC/AIR INTERFACE

Journal of Enhanced Heat Transfer ◽

10.1615/jenhheattransf.2020032648 ◽

2020 ◽

Vol 27 (3) ◽

pp. 267-288

Author(s):

Tahar Boussoukaia ◽

Belkacem Draoui ◽

M. Hamouda

Keyword(s):

Convective Flow ◽

Air Interface

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SIMULASI NEURAL NETWORK UNTUK PREDIKSI LOKASI USER PADA MOBILE NETWORK

TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi Kendali Komputer Elektrik dan Elektronika ◽

10.25124/tektrika.v9i1.238 ◽

2016 ◽

Vol 9 (1) ◽

Author(s):

Alexander Driyarkoro ◽

Nurain Silalahi ◽

Joko Haryatno

Keyword(s):

Neural Network ◽

Mobile Station ◽

Mobile Network ◽

Learning Rate ◽

Noise Factor ◽

Optimal Learning ◽

Air Interface ◽

Location Area ◽

Optimal Learning Rate

Prediksi lokasi user pada mobile network merupakan hal sangat penting, karena routing panggilan pada mobile station (MS) bergantung pada posisi MS saat itu. Mobilitas MS yang cukup tinggi, terutama di daerah perkotaan, menyebabkan pencarian (tracking) MS akan berpengaruh pada kinerja sistem mobile network, khususnya dalam hal efisiensi kanal kontrol pada air interface. Salah satu bentuk pencarian adalah dengan mengetahui perilaku gerakan yang menentukan posisi MS. Dari MSC/VLR dapat diketahui posisi MS pada waktu tertentu. Karena location area suatu MS selalu unik dari waktu ke waktu, dan hal itu merupakan perilaku (behaviour) MS, maka dapat dibuat profil pergerakannya. Dengan menggunakan Neural Network (NN) akan diperoleh location area MS pada masa yang akan datang. Model NN yang digunakan pada penelitian ini adalah Propagasi Balik. Beberapa parameter NN yang diteliti dalam mempengaruhi kinerja prediksi lokasi user meliputi noise factor, momentum dan learning rate. Pada penelitian ini diperoleh nilai optimal learning rate = 0,5 dan noise factor = 1.

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