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 - Scopus: 3Improved Successive Stream Selection With Quantized Channel in Heterogeneous Networks(Institute of Electrical and Electronics Engineers Inc., 2015) Aycan, Esra; Özbek, Berna; Le Ruyet, DidierThis paper focuses on different distortion metrics in order to analyze the influence of the imperfect channel state information (CSI) on the improved successive stream selection algorithm that manages the interference in a heterogeneous network. The presented approach initially selects the streams from the user of the pico cell, continuing with the strongest streams among the remaining streams that increase the sum rate and satisfy the constraint that at least one stream is selected from each user. In order to reduce the interference, the channel matrices of the remaining streams are projected orthogonally to the virtual transmit and receive channels of the selected stream. The impact of the quantization distortion on the precoding and postcoding design is examined. The performance of two distortion metrics which are the Chordal distance and the Euclidean distance are compared for different number of quantization bits. The performance evaluations are obtained by considering different locations of small cells with respect to the macro cell.Article Citation - Scopus: 1On Stream Selection for Interference Alignment With Limited Feedback in Heterogeneous Networks(John Wiley and Sons Inc., 2016) Aycan, Esra; Özbek, Berna; Le Ruyet, DidierThis paper presents a stream selection based interference alignment approach with imperfect channel state information for heterogeneous networks. The proposed solution constructs stream sequences by selecting only the strongest stream of each user where the first stream of the constructed stream sequences is associated to a pico user. While selecting the streams, the channel matrices of the unselected streams are projected orthogonally to the virtual transmit and receive channels of the selected stream in order to align the interference in the null space of these virtual channels. In addition, the influence of imperfect channel state information on the proposed algorithm is analysed. A bit allocation scheme is given by deriving an upper bound on the rate loss because of quantisation. The simulation results are carried out by considering various scenarios with different locations of pico cells at the cell edge regions of the macro cell. The performance results show that the proposed algorithm with the imperfect channel state information achieves higher performance than the existing algorithms.