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.

数字信号处理 = Understanding digital signal processing

An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique

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.

Maximum Likelihood Receiver for Multiple Channel Transmission Systems

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.

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

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.

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

This paper presents a MIMO two-way relaying scheme where the transceiver filter at the Relay Station (RS) processes the data using a variant of the Affine Projection Algorithm (APA), known as Partial Rank Algorithm (PRA) The relaying technique used is Amplify and Forward scheme (AF) The modulation technique used at the nodes S 1 and S 2 is Quadrature Phase Shift Keying (QPSK) The performance of PRA algorithm is verified by comparing its Mean-Square error performance (MSE) and Bit Error Rate (BER) with that of a transceiver filter based on Normalized Least Mean Square (NLMS) algorithm The computational complexity of the algorithm is also tabulated

An Affine Projection Algorithm based transceiver filter for MIMO two-way relaying scheme

Orthogonal Frequency Division Multiplexing (OFDM) is seen as one of the most promising solution to broadband wireless communications. Its performance depends on the channel state information (CSI) which can be estimated using different channel estimation algorithms. This paper proposes a Set Member Feasibility (SMF) formulation to govern the updating of the adaptive-filter coefficients. Here, two- dimensional Set-Membership Normalized LMS (2D-SM-NLMS) algorithm is proposed. The 2D-RLS adaptive channel estimation algorithm is also simulated for comparison. Matlab simulations show that 2D-SM-NLMS and 2D-RLS algorithms have similar Bit Error Rate (BER) performance. But the proposed algorithm has computational complexity of O(N) which is less than that of conventional 2D-RLS algorithm having order O(N 2 ) while

Two-Dimensional Set Membership Normalized Least Mean Square Adaptive Channel Estimation for OFDM Systems

The non-orthogonal multiple access (NOMA) scheme has been considered as a study item in 3rd generation partnership project (3GPP) for its possible use in fifth-generation new radio (5G NR) and beyond In this paper, we explore the available scalable bandwidth feature of 5G NR and propose a hybrid NOMA-OMA scheme based downlink data transmission in 5G NR The hybrid NOMA-OMA based seamless data transmission over the same 5G NR frame is carried out with help of different numerology factor which indirectly controls the subcarrier spacing, without affecting the conventional OMA based transmission Performance of the proposed method in conventional 5G NR system is analyzed in terms of the average bit error rate (BER) and ergodic capacity performance for various modulation schemes The results are illustrated for different µ used in 5G NR specifications Moreover, the obtained results are corroborated with the analytically obtained expressions

Performance Analysis of Hybrid NOMA-OMA Scheme for 5G NR System

In this letter, we study a few properties of Complex Conjugate Pair Sums (CCPSs) and Complex Conjugate Subspaces (CCSs). Initially, we consider an LTI system whose impulse response is one period data of CCPS. For a given input $x(n)$ , we prove that the output of this system is equivalent to computing the first order derivative of $x(n)$ . Further, with some constraints on the impulse response, the system output is also equivalent to the second order derivative. With this, we show that a fine edge detection in an image can be achieved using CCPSs as impulse response over Ramanujan Sums (RSs). Later computation of projection for CCS is studied. Here the projection matrix has a circulant structure, which makes the computation of projections easier. Finally, we prove that CCS is shift-invariant and closed under the operation of circular cross-correlation.

On Complex Conjugate Pair Sums and Complex Conjugate Subspaces

This paper proposes the precoder for Multi User-MassiveMIMO (MU-MaMI) frequency selective broadcast channels with few User Terminals(UTs) having single antenna each. The frequency selective effect of the MaMI channel will be manifested as ISI and IUI. In order to mitigate these interferences and to eliminate the group level decoding of UTs, a precoder is proposed utilizing the block time Channel Impulse Responses (CIRs). This proposed precoder is designed based on the following approaches, Matched Filter(MF), Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Regularized Channel Inversion (RCI). The performance of these precoders is evaluated using single symbol time (one time symbol) and block time symbols (N time symbols) of each UT. The optimal length N block time symbols and minimum number of Base Station (BS) antennas required for a given N block time and multipath L is evaluated with respect to the Signal-to-Interference Noise Ratio (SINR) asymptotic limits.

Vijay Kumar Chakka

Papers

An Affine Projection Algorithm based transceiver filter for MIMO two-way relaying scheme

Two-Dimensional Set Membership Normalized Least Mean Square Adaptive Channel Estimation for OFDM Systems

Performance Analysis of Hybrid NOMA-OMA Scheme for 5G NR System

On Complex Conjugate Pair Sums and Complex Conjugate Subspaces

Space-time block based linear precoders for multi user-MassiveMIMO frequency selective broadcast channels