Chandra R. Murthy

Indian Institute of Science

Mathematical optimizationCommunication channelMathematicsComputer scienceReal-time computing

180Publications

16H-index

1,277Citations

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Publications 184

Newest

#1Rajshekhar Vishweshwar BhatH-Index: 4

#2Mehul MotaniH-Index: 31

Last. Rahul VazeH-Index: 18

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#1R. Ramu Naidu (IIP: Indian Institute of Petroleum)

#2Chandra R. Murthy (IISc: Indian Institute of Science)H-Index: 16

Abstract In this paper, we propose a method to construct uni-modular tight frames (UMTFs), which are tight frames with the additional constraint that every entry of the matrix has the same magnitude. UMTFs are useful in many applications, since multiplication of a UMTF by a vector can be implemented in polar coordinates using very low computational cost. Since normalized UMTFs are unit norm tight frames (UNTFs), and since a UNTF is a minimizer of the frame potential, we propose an algorithm to f...

#1Geethu Joseph (SU: Syracuse University)H-Index: 1

#2Chandra R. Murthy (IISc: Indian Institute of Science)H-Index: 16

Dictionary learning (DL) is a well-researched problem, where the goal is to learn a dictionary from a finite set of noisy training signals, such that the training data admits a sparse representation over the dictionary. While several solutions are available in the literature, relatively little is known about their convergence and optimality properties. In this paper, we make progress on this problem by analyzing a Bayesian algorithm for DL. Specifically, we cast the DL problem into the sparse Ba...

#1Geethu JosephH-Index: 1

#2Chandra R. Murthy (IISc: Indian Institute of Science)H-Index: 16

In this work, we consider the controllability of a discrete-time linear dynamical system with sparse control inputs. Sparsity constraints on the input arises naturally in networked systems, where activating each input variable adds to the cost of control. We derive algebraic necessary and sufficient conditions for ensuring controllability of a system with an arbitrary transfer matrix. The derived conditions can be verified in polynomial time complexity, unlike the more traditional Kalman-type ra...

#2Prabhu BabuH-Index: 18

Last. Chandra R. MurthyH-Index: 16

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In this paper, we study the problem of downlink (DL) sum rate maximization in codebook based multiuser (MU) multiple input multiple output (MIMO) systems. The user equipments (UEs) estimate the DL channels using pilot symbols sent by the access point (AP) and feedback the estimates to the AP over a control channel. We present a closed form expression for the achievable sum rate of the MU-MIMO broadcast system with codebook constrained precoding based on the estimated channels, where multiple dat...

Quantized Variational Bayesian Joint Channel Estimation and Data Detection for Uplink Massive MIMO Systems with Low resolution ADCS

#2Chandra R. MurthyH-Index: 16

Last. Ramesh AnnavajjalaH-Index: 1

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In this paper, we consider the joint channel estimation and data detection in an uplink massive multiple input multiple output (MIMO) receiver with low resolution analog to digital converters (ADCs). The nonlinearities introduced by the ADCs make the existing linear multiuser detection (MUD) approaches suboptimal, and motivates a fresh look at the problem. Also, channel state information is necessary to obtain the channel quality metrics that are used for link adaptation by the base station (BS)...

#1David GesbertH-Index: 51

#2Deniz GündüzH-Index: 1

Last. Nikolaos D. SidiropoulosH-Index: 50

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In this paper, we present a computationally efficient sparse signal recovery scheme using Deep Neural Networks (DNN). The architecture of the introduced neural network is inspired from sparse Bayesian learning (SBL) and named as Learned-SBL (L-SBL). We design a common architecture to recover sparse as well as block sparse vectors from single measurement vector (SMV) or multiple measurement vectors (MMV) depending on the nature of the training data. In the MMV model, the L-SBL network can be trai...

#1David Gesbert (EURECOM: Institut Eurécom)H-Index: 51

#2Deniz Gunduz (Imperial College London)H-Index: 33

Last. Nikolaos D. Sidiropoulos (UVA: University of Virginia)H-Index: 50

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Machine learning and data driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. Yet, the evolution towards learning-based data driven networks is still in its infancy, and much of the realization of the promised benefits requires thorough research and development. Fundamental questions remain as to where and how ML can really complement the well-established, well-tested communication systems designed over the last four decades. Moreo...

#1Deniz Gunduz (Imperial College London)H-Index: 33

#2Paul de Kerret (EURECOM: Institut Eurécom)H-Index: 9

Last. Mihaela van der Schaar (UCLA: University of California, Los Angeles)H-Index: 42

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Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story – ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these te...

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