Bulgaristan'ın Vidin Şehrinde Halil ve İbrahim İkiz Kardeşlerin Kurdukları Kültür Vakfı ve Onun "Şefkat" Kıraathanesi

[No Abstract Available

Joint estimation of multiple RF impairments using deep multi-task learning

Radio-frequency (RF) front-end forms a critical part of any radio system, defining its cost as well as communication performance. However, these components frequently exhibit non-ideal behavior, referred to as impairments, due to the imperfections in the manufacturing/design process. Most of the designers rely on simplified closed-form models to estimate these impairments. On the other hand, these models do not holistically or accurately capture the effects of real-world RF front-end components. Recently, machine learning-based algorithms have been proposed to estimate these impairments. However, these algorithms are not capable of estimating multiple RF impairments jointly, which leads to limited estimation accuracy. In this paper, the joint estimation of multiple RF impairments by exploiting the relationship between them is proposed. To do this, a deep multi-task learning-based algorithm is designed. Extensive simulation results reveal that the performance of the proposed joint RF impairments estimation algorithm is superior to the conventional individual estimations in terms of mean-square error. Moreover, the proposed algorithm removes the need of training multiple models for estimating the different impairments

Waveform design with constellation extension for OFDM dual-functional radar-communications

Orthogonal frequency division multiplexing (OFDM) is widely used and works efficiently for the communication, but emerging applications requires OFDM to be flexible to meet sensing requirements. The time-frequency waveform design of OFDM for dual-functional radar-communications (DFRC) is critical to achieve the future communication and sensing requirements. Therefore, we propose a novel method to minimize Cramér-Rao bounds (CRBs) of the delay and Doppler estimation to improve radar performance of an OFDM DFRC system. Although some methods are proposed in the literature to improve the CRBs, these methods either require feedforward signaling or subcarrier reservation. However, it is possible to exploit the constellation extension of quadrature amplitude modulation (QAM) to achieve lower CRBs without these requirements. Therefore, the proposed method provides a transparent communication along with the CRB minimization for conventional OFDM systems. For the evaluation of the proposed method, CRB and symbol error rate (SER) are considered in the simulation results. Furthermore, the theoretical SER analysis of the proposed method is derived to understand the effects of CRB minimization on the communication performance

Identification of distorted RF components via deep multi-task learning

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios

Bulgaristan Müslümanlarının Dini Teşkilatlarını ve Kurumlarını Düzenleyen Nizamname

[No Abstract Available

Bulgaristan Türklerinin eğitimi

Donated by Klaus Kreise

Bulgaristan'da Cemaati İslamiye ve Vakıf Kurumları

[No Abstract Available

Bulgaristan'da Türk Kültür ve Sanat Eserleri

[No Abstract Available

Bulgaristan'da Müftülükler ve Şer'iyye Mahkemeleri

[No Abstract Available

Guard band reduction for 5G and beyond multiple numerologies

The existence of inter-numerology interference (INI) is a major drawback for the flexible multi-numerology frame structure proposed for the upcoming fifth generation New Radio (5G-NR). Insertion of a guard band (GB) between adjacent numerologies has been widely used in the literature as one of the effective ways to reduce the INI. However, the conventional way of implementing GBs is inefficient in terms of spectrum usage. In this letter, we exploit the inherent INI characteristics of the scalable multi-numerology structure to propose a more spectrally efficient way of implementing GBs. It is shown through simulations that the proposed GB insertion technique enhances the GB utilization up to 50% while achieving the same bit error rate performance as the conventionally implemented GB.218E03