Vijay Kumar Chakka

Bio: Vijay Kumar Chakka is an academic researcher from Shiv Nadar University. The author has contributed to research in topics: Orthogonal frequency-division multiplexing & MIMO. The author has an hindex of 6, co-authored 49 publications receiving 201 citations. Previous affiliations of Vijay Kumar Chakka include Indian Institute of Chemical Technology & Dhirubhai Ambani Institute of Information and Communication Technology.

MIMO two-way relaying in frequency selective environment using OFDM

Abstract: This paper presents Orthogonal Frequency Division Multiplexing (OFDM) based two-way relaying scheme to communicate between two nodes S 1 and S 2 via MIMO Relay Station (RS) in a frequency selective environment. This scheme relies on two-hop relaying approach which uses two orthogonal channel resources to transmit and receive a signal. This scheme requires the channel state information (CSI) only at RS. Spatial filtering is implemented at the RS for transmit and receive processing which is evaluated using zero forcing (ZF) and minimum mean squared error (MMSE) criterion. MATLAB simulations are conducted to evaluate the validity of the algorithm using both criterions in presence of frequency selective environment. The simulation results depict that the algorithm under defined conditions performs better in a frequency selective fading environment with the use of OFDM. Also, the BER performance evaluated through spatial filtering done using MMSE is found to be better than its ZF counterpart.

Inverse QR 2D-RLS Adaptive Channel Estimation for OFDM Systems

Abstract: This letter proposes Inverse QR two-dimensional Recursive Least Square (IQR-2D-RLS) adaptive channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) systems (using Givens Rotations and Householder Transformations). It is more stable numerically than 2D-RLS algorithm. MATLAB simulations show that BER performance of IQR-2D-RLS algorithm is similar to that of 2D-RLS algorithm.

A New Signal Representation Using Complex Conjugate Pair Sums

Abstract: This letter introduces a real valued summation known as complex conjugate pair sum (CCPS) . The space spanned by CCPS and its one circular downshift is called complex conjugate subspace (CCS) . For a given positive integer $N\geq 3$ , there exists $\frac{\varphi (N)}{2}$ CCPSs forming $\frac{\varphi (N)}{2}$ CCSs, where $\varphi (N)$ is the Euler's totient function . We prove that these CCSs are mutually orthogonal and their direct sum form a $\varphi (N)$ dimensional subspace $s_N$ of $\mathbb {C}^N$ . We propose that any signal of finite length $N$ is represented as a linear combination of elements from a special basis of $s_d$ , for each divisor $d$ of $N$ . This defines a new transform named as complex conjugate periodic transform (CCPT) . Later, we compared CCPT with discrete Fourier transform (DFT) and Ramanujan periodic transform (RPT). It is shown that, using CCPT, we can estimate the period, hidden periods, and frequency information of a signal. Whereas, RPT does not provide the frequency information. For a complex valued input signal, CCPT offers computational benefit over DFT. A CCPT dictionary based method is proposed to extract non-divisor period information.

Low Complexity 2D Adaptive Channel Estimation for OFDM Based Two-Way Relay Systems

Abstract: This paper introduces a low complexity adaptive channel estimation technique for OFDM based two-way relay systems. The adaptive filter used is known as Group Fast Array Multichannel 2D-Recursive Least Square (GFAM 2DRLS) filter. It has a computational complexity comparable to that of 2D-Normalized Least Mean Square (2D-NLMS) algorithm while maintaining the same convergence rate as the classic 2D Recursive Least Square (2D-RLS) algorithm. It considers the correlation of the channel frequency response in both time and frequency, while estimating the channel. In order to reduce the number of training data for time varying channel, the channel estimation is carried out based on the Decision Directed (DD) principle. It is assumed that the relay is capable of performing complex signal processing tasks. Hence the channel estimation is performed at the relay. Since the Channel State Information (CSI) is available at the relay, it could perform Multiple Input Multiple Output (MIMO) precoding of the transmitted data. Hence CSI is not required at the transmitting nodes. The convergence rate of GFAM 2D-RLS is compared with the existing 2D-NLMS algorithm and the computational complexity at each iteration is tabulated. Simulations are performed using MATLAB.

Performance Comparison of OMP and CoSaMP Based Channel Estimation in AF-TWRN Scenario

Abstract: We propose a new way to estimate the sparse multipath channel of an Amplify and Forward Two Way Relay Network (AF-TWRN) based on Compressive Sensing (CS), using Compressive Sampling Matching Pursuit (CoSaMP). MSE based performance of CoSaMP and OMP methods and corresponding BPSK based BER performance of the system using the estimated channels are plotted. CoSaMP based channel estimation is performing similar to OMP based channel estimation, with lesser time complexity, in both MSE and BER measures of the system, when the sparsity/worst case sparsity of the channel is known.

数字信号处理 = Understanding digital signal processing

Abstract: From the Publisher: This is undoubtedly the most accessible book on digital signal processing (DSP) available to the beginner. Using intuitive explanations and well-chosen examples, this book gives you the tools to develop a fundamental understanding of DSP theory. The author covers the essential mathematics by explaining the meaning and significance of the key DSP equations. Comprehensive in scope, and gentle in approach, the book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.

Maximum Likelihood Receiver for Multiple Channel Transmission Systems

Abstract: A maximum likelihood (ML) estimator for digital sequences disturbed by Gaussian noise, intersymbol interference (ISI) and interchannel interference (ICI) is derived. It is shown that the sampled outputs of the multiple matched filter (MMF) form a set of sufficient statistics for estimating the input vector sequence. Two ML vector sequence estimation algorithms are presented. One makes use of the sampled output data of the multiple whitened matched filter and is called the vector Viterbi algorithm. The other one is a modification of the vector Viterbi algorithm and uses directly the sampled output of the MMF. It appears that, under a certain condition, the error performance is asymptotically as good as if both ISI and ICI were absent.

An Optimal Low Complexity PAPR Reduction Technique for Next Generation OFDM Systems

Abstract: Orthogonal frequency division multiplexing (OFDM) is an efficient multi-carrier modulation technique that underlies most of the current and probably future high-speed wireless communication systems. However, the OFDM waveform is characterized by a high peak-to-average power ratio (PAPR), especially when a large number of subcarriers are used. A high PAPR is a major waveform defect since it leads to non-linear distortion when passing through the transmitter’s power amplifier. Most of the PAPR reduction techniques found in the literature reduce the PAPR mainly at the cost of either excessive computational complexity or degrading the transmission bit error rate (BER). We propose a low-complexity technique for PAPR reduction based on linear scaling of a portion of signal coefficients by an optimal factor. This paper is backed up by the extensive analysis of various performance metrics, which leads to optimal choices of key parameters and hence maximum achievable gains. The analytic and simulated results show that the proposed technique is capable of reducing the PAPR effectively with negligible effect on BER in return for a slight reduction in data rate. For example, for 1024 subcarriers, the PAPR can be reduced from 13 dB to below 7.4 or 6.9 dB, in return for only 1% or 2% reduction in data rate, respectively. In addition, the achievable PAPR varies very slightly in response to increasing the number of subcarriers. This offers a highly competitive and flexible tradeoff compared with those provided by current techniques found in the literature. Therefore, this technique has a very good potential for practical application in current and future OFDM-based systems, especially those which employ a very large number of subcarriers, such as LTE, DVB-T2, and 5G systems.

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Abstract: With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.