V
Vimal Bhatia
Researcher at Indian Institute of Technology Indore
Publications - 351
Citations - 3214
Vimal Bhatia is an academic researcher from Indian Institute of Technology Indore. The author has contributed to research in topics: Computer science & Bit error rate. The author has an hindex of 21, co-authored 302 publications receiving 2053 citations. Previous affiliations of Vimal Bhatia include Netaji Subhas Institute of Technology & Indian Institute of Technology Delhi.
Papers
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Impact of Imperfect CSI on ASER of Hexagonal and Rectangular QAM for AF Relaying Network
TL;DR: The closed-form expression of outage probability for a dual-hop variable-gain amplify-and-forward relay network is derived from the maximal ratio combining receiver over independent and non-identically distributed frequency flat Nakagami-m fading channels with integer-valued fading parameter and imperfect channel state information.
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Low Complexity Post-Distorter for Visible Light Communications
Rangeet Mitra,Vimal Bhatia +1 more
TL;DR: This letter proposes a low-complexity reproducing kernel Hilbert space-based post-distorter using a better sparsification technique equipped with an adaptive kernel width optimized by a minimum symbol error rate (MSER) criterion.
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Adaptive Precoding-Based Detection Algorithm for Massive MIMO Visible Light Communication
TL;DR: Simulations show that the proposed adaptive precoding technique exhibits superior BER performance over existing techniques, and Analytical upper bounds for BER are derived and validated by simulations.
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On Impact of Imperfect CSI Over Hexagonal QAM for TAS/MRC-MIMO Cooperative Relay Network
TL;DR: This letter investigates the effect of imperfect channel state information (CSI) on quadrature amplitude modulation (QAM) schemes for a multi-input and multi-output (MIMO) variable gain non-regenerative cooperative network over the generalized Nakagami-m fading channel.
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Mixture-Kernel Based Post-Distortion in RKHS for Time-Varying VLC Channels
TL;DR: A novel RKHS based post-distorter that adaptively learns a sparse Dictionary based on the incoming observations, and monitors validity of the dictionary based on a proposed metric in RK HS is proposed.