Person:
Vasavada, Yash

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Yash Vasavada

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079-68261634

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Communication, Signal Processing, Machine Learning

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Biography

Yash Vasavada is currently a Professor at DAIICT, and he works in the areas of communication system design and development and application of machine learning algorithms to communications and signal processing. He has over twenty years of experience at Hughes Networks systems in Germantown, Maryland, USA, where he has worked on design, development and deployment of a number of GEO, MEO and LEO satellite systems. At Hughes, Yash has published several journal and conference papers, and he has been granted twelve US Patents. Yash has obtained B. E. degree in Electronics and Communications from L. D. Engineering College, Ahmedabad, and M.S. and Ph.D. degree from Virginia Polytechnic Institute and State University (Virginia Tech) in USA.

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2020 - 20243

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Now showing 1 - 3 of 3
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    Low Complexity Optimal and Suboptimal Detection at Spatial Modulation MIMO Receivers
    (IEEE, 01-04-2022) Vasavada, Yash; John, Bibin Baby; Vasavada, Yash; Vasavada, Yash; Vasavada, Yash; Vasavada, Yash; Vasavada, Yash; DA-IICT, Gandhinagar; John, Bibin Baby (201721017)
    This paper develops three low-complexity detection schemes for the spatial modulation (SM) Multiple Input Multiple Output (MIMO) systems. We first propose a reduced complexity maximum likelihood (RC-ML) detection scheme. For the practical values of the modulation index, the proposed RC-ML method is simpler than the existing hard-limiting-based ML schemes. The two other proposals are based on a suboptimal Maximum Ratio Combining (MRC) detection method. We present a new analysis of the MRC detector in the uncorrelated Rayleigh fading channel and identify the system operational scenarios for which the MRC performance is nearly identical to the ML performance. Our proposals � a hybrid MRC-ML method and an MRC scheme with rank reduction � are inspired by this analysis. The analytical and simulation results show that the proposed MRC-based schemes can attain a near-optimal performance while reducing the complexity of the ML-based SM receiver.
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    A Low-Complexity Blind Iterative Approach for Receive-Side Hybrid Beamforming
    (IEEE, 15-04-2024) Vasavada, Yash; Dhami, Aarushi; Reed, Jeffrey H; Vasavada, Yash; Vasavada, Yash; Vasavada, Yash; Vasavada, Yash; Vasavada, Yash; DA-IICT, Gandhinagar; Dhami, Aarushi (201921008)
    This paper introduces a novel blind iterative projections algorithm to address challenges associated with codebook-based beamforming in 5G and beyond systems. In contrast to existing methodologies, our proposed algorithm eliminates quantization noise and search space scanning latency. The blind nature of our approach capitalizes on information-bearing symbols, yielding performance close to theoretical expectations. In contrast to the prevalent iterative least squares (LS) algorithms in the literature, our proposal does not necessitate the constant modulus or finite alphabet constraints on the desired signal. This makes our algorithm more general and allows us to demonstrate its stability analytically and capability to perform singular value decomposition (SVD) of the received signal matrix. Our algorithm achieves SVD optimality with lower computational costs than conventional SVD methods. We present an analysis of the Signal to Noise Ratio (SNR) at each iteration, elucidating the impact of batch size and noise variance on the SNR gain at convergence. We apply our proposed algorithm to receive-side hybrid beamforming and show that it offers superior performance compared to various existing approaches documented in the literature.
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    Sub-Nyquist Spectrum Sensing of Sparse Wideband Signals Using Low-Density Measurement Matrices
    (IEEE, 12-06-2020) Vasavada, Yash; Prakash, Chandra; Vasavada, Yash; Vasavada, Yash; Vasavada, Yash; Vasavada, Yash; Vasavada, Yash; DA-IICT, Gandhinagar
    The problem of wideband spectrum sensing/sampling in the sub-Nyquist domain is solved in this paper using sparse (low density) binary-valued measurement matrices. Key objectives are (i) to achieve an efficient compression ratio, and (ii) improve the signal reconstruction performance. We propose a novel RF front-end with parallel branches that we have called Low-Density Wideband Converter (LDWC). We show that the LDWC implements a binary Low-Density Parity Check (LDPC) matrix as the compressive sensing (CS) measurement matrix. We evaluate, using an Information Theoretic approach, the asymptotic bound on the required number of LDWC parallel branches for sparsity detection. We develop two new belief propagation (BP) algorithms that operate on the Tanner graph of the CS measurements. We have derived the first algorithm by assuming independence among the variable nodes (VNs) of the Tanner graph. For the second method, we have accounted for the joint probability distribution of the VNs. Analytical and simulated performance results prove the concepts of the LDWC and the proposed BP algorithms and quantify the attainment of objectives (i) and (ii) stated above.