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Sökning: WFRF:(Shankar M. R. Bhavani)

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  • Elbir, Ahmet M., et al. (författare)
  • A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO
  • 2021
  • Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; , s. 1-1
  • Tidskriftsartikel (refereegranskat)abstract
    • 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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  • Piazza, R., et al. (författare)
  • Multi-Gateway Data Predistortion for Non-Linear Satellite Channels
  • 2015
  • Ingår i: IEEE Transactions on Communications. - : IEEE. - 0090-6778 .- 1558-0857. ; 63:10, s. 3789-3802
  • Tidskriftsartikel (refereegranskat)abstract
    • Joint on-board amplification of multiple carriers reduces hardware and weight, enabling cost-efficient satellite architectures. However, on-board power amplification is inherently a non-linear operation and the distortion effects drastically increase when the high-power amplifier (HPA) is operated in multiple-carrier mode due to the generated inter-modulation products. In order to achieve the desired on-board power efficiency and to reduce the non-linear distortion, specific countermeasures need to be put in place. Emerging multi-gateway satellite systems where, in the most general case, each carrier is uplinked independently from a different gateway, would gain further flexibility by adopting a multicarrier architecture. While advanced predistortion techniques are already available for the single-gateway scenario, no solution has been proposed for the multi-gateway multicarrier scenario. In this work, we propose novel distributed predistortion techniques to improve spectral and power efficiency in multi-gateway non-linear satellite channels. Further, we analyze the multiple-carrier predistortion parameter estimation error with respect to noise sensitivity, proposing a robust version of the indirect learning method. Distributed processing becomes sensitive to synchronization amongst the actors, and we present an evaluation of the sensitivity of proposed techniques to imperfections.
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  • Resultat 1-10 av 44

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