Yazar "Shankar, M. R. Bhavani" seçeneğine göre listele

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    A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Elbir, Ahmet M.; Mishra, Kumar Vijay; Shankar, M. R. Bhavani; Ottersten, Bjorn
    Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.
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    The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and Communications Paradigms
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Elbir, Ahmet M.; Mishra, Kumar Vijay; Shankar, M. R. Bhavani; Chatzinotas, Symeon
    The intelligent reflecting surface ( IRS) alters the behavior of wireless media and, consequently, has potential to improve the performance and reliability of wireless systems such as communications and radar remote sensing. Recently, integrated sensing and communications (ISAC) has been widely studied as a means to efficiently utilize spectrum and thereby save cost and power. This article investigates the role of IRS in the future ISAC paradigms. While there is a rich heritage of recent research into IRS-assisted communications, the IRS- assisted radars and ISAC remain relatively unexamined. We discuss the putative advantages of IRS deployment, such as coverage extension, interference suppression, and enhanced parameter estimation, for both communications and radar. We introduce possible IRS- assisted ISAC scenarios with common and dedicated surfaces. The article provides an overview of related signal processing techniques and the design challenges, such as wireless channel acquisition, waveform design, and security.