Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/47

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Citation Count: 1
An open software architecture of neural networks: Neurosoft
(2004) Arsan, Taner; Arsan, Taner; Saydam, Tuncay
Software architecture of generic distributed neural networks and its relevant information model have been developed. Principles of on-line architecture building training controlling (managing) and topological optimization guidelines are provided and extensively discussed.
Citation Count: 2
Power Control and Resource Allocation in TDD-OFDM Based Femtocell Networks with Interference
(IEEE, 2017) Arsan, Taner; Arsan, Taner; Panayırcı, Erdal
Femtocell technology is a promising solution for different dilemmas in cellular networks. In femtocell power control the interference experienced by the network is divided into two main tiers according to the type of network whose signal is interfering with another network. In utilizing the functionality of a two-tier network where femtocell technology is deployed a major challenge is in sharing the frequency resource of a macrocell. This paper proposes an enhanced dynamic algorithm bounded by two constraints to optimize the transmission powers of femtocell users in TDD-OFDM based femtocell networks taking into consideration rate enhancement of femtocell mobile stations. We compare our algorithm with the macrocell guard system which allows femtocells to occupy only the subchannels unoccupied by the macrocell.
Citation Count: 2
Subchannel Allocation and Power Control for Uplink Femtocell Radio Networks with Imperfect Channel State Information
(Springer, 2019) Arsan, Taner; Arsan, Taner; Panayırcı, Erdal
Femtocell technology is emerging as a key solution for mobile operators for its advantage in coverage and capacity enhancement along with its cost effectiveness. However, densely and randomly deployed femtocells while sharing the frequency spectrum of the macrocell arises a severe interference environment. In femtocells deployment, interference coming from a femtocell user affect other femtocell users and the macrocell users, where maintaining the communication of the users in both tiers is a mandatory. In this paper, a novel power control algorithm is proposed for optimizing the uplink transmission powers of femtocell users in a TDD-OFDM communication model in the presence of a channel estimation error and intra-tier interference. We consider signal to interference and noise ratio as the objective function where the proposed constraints deal with: (1) the aggregated interference coming from femtocell tier and received at the active subchannels by the macrocell tier, and (2) the maximum uplink power a femtocell user equipment is allowed to occupy per admissible subchannel. Based on Lagrangian multipliers, the proposed power control approach grants the priority in subchannel usage for macrocell user, then it allows or prohibits frequency reuse of a subchannel with the femtocell tier. A comparison is then made with a pure isolation method that does not allow femtocell user equipments to occupy the active subchannels at the macrocell tier. The numerical results of the proposed approach show a high total rate of femtocell user equipments and the average uplink power is below the maximum allowable transmission power.
Citation Count: 0
A systems software architecture for training neural fuzzy neural and genetic computational intelligent networks
(IEEE, 2006) Arsan, Taner; Öğrenci, Arif Selçuk; Saydam, Tuncay
A systems software architecture for training distributed neural fuzzy neural and genetic networks and their relevant information models have been developed. Principles of on-line architecture building training managing and optimization guidelines are provided and extensively discussed. Qualitative comparisons of neural training strategies have been provided.
Citation Count: 2
Transmitter source location estimation using crowd data
(Pergamon-Elsevier Science Ltd, 2018) Arsan, Taner; Arsan, Taner
The problem of transmitter source localization in a dense urban area has been investigated where a supervised learning approach utilizing neural networks has been adopted. The cellular phone network cells and signals have been used as the test bed where data are collected by means of a smart phone. Location and signal strength data are obtained by random navigation and this information is used to develop a learning system for cells with known base station location. The model is applied to data collected in other cells to predict their base station locations. Results are consistent and indicating a potential for effective use of this methodology. The performance increases by increasing the training set size. Several shortcomings and future research topics are discussed. (C) 2017 Elsevier Ltd. All rights reserved.

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