Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 2Citation - Scopus: 2Deep Learning Based Adaptive Bit Allocation for Heterogeneous Interference Channels(Elsevier, 2021) Aycan, Esra; Özbek, Berna; Le Ruyet, DidierThis paper proposes an adaptive bit allocation scheme by using a fully connected (FC) deep neural network (DNN) considering imperfect channel state information (CSI) for heterogeneous networks. Achieving an accurate CSI has a crucial role on the system performance of the heterogeneous networks. Different quantization techniques have been employed to reduce the feedback overhead. However, the system performance cannot increase linearly with the number of bits increasing exponentially. Since optimizing the total number of bits is too complex for the entire network, an initial step is performed to distribute the bits to each cell in the conventional method. Then, the distributed bits are further allocated to each channel optimally. In order to enable direct allocation for the entire network, a FC-DNN based method is presented in this study. The optimized number of bits can be directly obtained for a different number of bits and scenarios by the proposed approach. The simulations are performed by using various scenarios with different allocation schemes. The performance results show that the DNN based method achieves a closer performance to the conventional approach. (C) 2021 Elsevier B.V. All rights reserved.Conference Object Citation - WoS: 5Citation - Scopus: 3Hierarchical Successive Stream Selection for Heterogeneous Network Interference(Institute of Electrical and Electronics Engineers Inc., 2014) Aycan, Esra; Özbek, Berna; Le Ruyet, DidierThis paper presents a hierarchical stream selection approach to deal with the interference in a heterogeneous network where different cell types are coexisting with each other to increase the sum capacity. Due to the variety of the transmit powers between the macro and small cells, interference levels are different. The proposed solution hierarchically selects the strongest streams of each cell with a contribution to the sum rate, while constructing the streams via singular value decomposition (SVD). In order to reduce the interference, the channel matrices of the remaining streams are projected orthogonally to the virtual transmit channel and virtual receive channel of the selected stream. The performance evaluations are obtained by considering different locations of small cells with respect to the macro cell. It is shown that the proposed method can dynamically select more streams in heterogeneous networks and achieve higher data rates compared to the existing algorithms. © 2014 IEEE.Article Citation - WoS: 7Citation - Scopus: 8Performance Evaluation of Multicast Miso-Ofdm Systems(Springer Verlag, 2008) Özbek, Berna; Le Ruyet, Didier; Khanfir, HajerIn this paper, we analyze the performance of multicast orthogonal frequency division multiplexing (OFDM) systems with single and multiple transmit antennas. We show that the resource allocation that includes the subcarrier allocation, bit loading, and the precoding vector selection in the multiple-input single-output (MISO) case is a difficult optimization problem. Consequently, we propose suboptimal algorithms based on the maximization of the sum data rate and the maximization of the minimum user data rate criteria. For practical application, we consider a complete transmission chain by combining powerful erasure codes with the proposed algorithms. Using this scheme, we guarantee that each user receives the same amount of information to decode the same data. Simulation results show that, for both single-input single-output (SISO)-OFDM and MISO-OFDM cases, the proposed multicast OFDM systems achieve gains over the worst user case algorithm.