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Low complexity detector for amplitude phase shift keying-based differential spatial modulation

IET COMMUNICATIONS 2020年第20期14卷 3669-3675页

作者： Jose, Deepak Sameer, S. M. Natl Inst Technol Dept Elect & Commun Calicut 673601 Kerala India

Differential spatial modulation (DSM) involves the transmission of information through a combination of phase-modulated symbols and transmit antenna index. For efficient use of the wireless system resources, the amplitude information is also utilised as a part of the transmitted data in amplitude-phase shift keying-DSM (APSK-DSM). Here some of the information bits get encoded as differential amplitude in each of the transmit antennas, whenever they are activated resulting in better bandwidth efficiency than conventional DSM. However, prevailing detectors for APSK-DSM have high computational complexity. In this work, the authors proposed a low complexity detector for APSK-based DSM schemes using compressive sensing and an l2-norm minimisation for detecting the differential amplitude. To decode this amplitude information, the authors have formulated a column by column decomposition of the received symbol in terms of the amplitude- and phase-modulated symbols received from each transmit antenna. The error performance of the proposed detector is nearly identical to that of existing APSK-based DSM receivers, which use maximum-likelihood detection but with much lower computational complexity than the latter.

关键词： minimisation MIMO communication decoding error statistics maximum likelihood detection computational complexity transmitting antennas radio receivers amplitude shift keying phase shift keying wireless channels APSK-DSM information bits differential amplitude transmit antenna bandwidth efficiency conventional DSM high computational complexity low complexity detector APSK-based DSM schemes amplitude information received symbol phase-modulated symbols amplitude phase shift differential spatial modulation antenna index wireless system resources transmitted data amplitude-phase shift l2-norm minimisation APSK-based DSM receivers

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Practical reduced-complexity equaliser for EDGE

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ELECTRONICS LETTERS 2001年第9期37卷 593-594页

作者： Chow, NH Jeans, TG Tafazolli, R Univ Surrey Ctr Commun Syst Res Guildford GU2 7XH Surrey England

EDGE uses the 8PSK modulation scheme. It is difficult to employ optimum equalisation due to the high computation complexity required. Consequently, a sub-optimum technique offering an approximation to maximum likelihood sequence estimation performance is required. A simple. reduced complexity fractional spaced equaliser structure involving the delayed decision feedback sequence estimation is therefore presented. The Fractional spaced approach does not require additional pre-processing creating a minimum phase overall channel impulse. It is suitable for unknown channels and has near optimum performance.

关键词： reduced complexity fractional spaced equaliser structure delayed decision feedback sequence estimation near optimum performance enhanced data rules for GSM evolution minimum phase overall channel impulse phase shift keying Modulation and coding methods Mobile radio systems linearised Gaussian minimum shift keying minimum shift keying cellular radio maximum likelihood sequence estimation performance reduced-complexity equaliser communication complexity EDGE decision feedback equalisers optimum equalisation high computational complexity maximum likelihood sequence estimation Communication channel equalisation and identification 8PSK modulation scheme unknown channels

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Low-complexity signal detection for large-scale MIMO systems with second-order Richardson method

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ELECTRONICS LETTERS 2020年第9期56卷 467-469页

作者： Khoso, Imran A. Zhang, Xiaofei Shaikh, Abdul Hayee Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211100 Peoples R China

Linear minimum mean-square error (MMSE) detection achieves near-optimal performance in large-scale multiple-input multiple-output (LS-MIMO) systems but entails high computational complexity due to large matrix inversion operations. In this Letter, a novel computationally efficient algorithm based on second-order Richardson method is proposed to solve the LS-MIMO detection problem. While no a priori information for the first iteration of the second-order Richardson method is available, the conjugate gradient scheme is exploited that greatly reduces the number of iterations to achieve the desired performance. Moreover, the eigenvalue-based acceleration parameters are proposed to further accelerate the convergence rate. Numerical results demonstrate that the proposed detector outperforms the existing methods and approaches the performance of MMSE with a small number of iterations.

关键词： mean square error methods least mean squares methods signal detection matrix inversion MIMO communication iterative methods computational complexity eigenvalues and eigenfunctions large-scale MIMO systems second-order Richardson method linear minimum mean-square error detection achieves near-optimal performance multiple-input multiple-output systems high computational complexity matrix inversion operations computationally efficient algorithm LS-MIMO detection problem eigenvalue-based acceleration parameters low-complexity signal detection

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A Low-Delay Low-complexity EKF Design for Joint Channel and CFO Estimation in Multi-User Cognitive Communications

A Low-Delay Low-Complexity EKF Design for Joint Channel and ...

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54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Zhao, Pengkai Shen, Cong Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA Qualcomm Inc San Diego CA USA

ISBN: (纸本)9781424492688

Parameter estimation in cognitive communications can be formulated as a multi-user estimation problem, which is solvable under maximum likelihood solution but involves high computational complexity. This paper presents a time-sharing and interference mitigation based EKF (Extended Kalman Filter) design for joint CFO (carrier frequency offset) and channel estimation at multiple cognitive users. The key objective is to realize low implementation complexity by decomposing high-dimensional parameters into multiple separate low-dimensional estimation problems, which can be solved in a time-shared manner via pipelining operation. We first present a basic EKF design that estimates the parameters from one TX user to one RX antenna. Then such basic design is time-shared and reused to estimate parameters from multiple TX users to multiple RX antennas. Meanwhile, we use interference mitigation module to cancel the co-channel interference at each RX sample. In addition, we further propose adaptive noise variance tracking module to improve the estimation performance. The proposed design enjoys low delay and low buffer size (because of its online real-time processing), as well as low implementation complexity (because of time-sharing and pipeling design). Its estimation performance is verified to be close to Cramer-Rao bound.

关键词： Antennas CFO estimation Channel estimation complexity theory Cramer Rao bound Estimation Interference Kalman filters Noise measurement OFDM carrier frequency offset channel estimation channel estimation cognitive radio computational complexity extended Kalman filter high computational complexity joint channel low delay low complexity maximum likelihood estimation maximum likelihood solution multi user estimation problem multiuser channels multiuser cognitive communications parameter estimation time sharing

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Robust compressive multi-input-multi-output imaging

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IET RADAR SONAR AND NAVIGATION 2013年第3期7卷 233-245页

作者： Zhao, Guanghui Wang, Qi Shen, Fangfang Li, Xiaoming Shi, Guangming Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Aviat Key Lab Sci & Technol AISSS Wuxi 214063 Peoples R China

Canonical multi-input-multi-output (MIMO) imaging methods suffer from limited resolution, poor robustness against noise and high computational complexity, especially when the array aperture is limited (affecting angular resolution) and the number of snapshots is limited (affecting Doppler resolution). In this study, the authors discuss a new range-angle-Doppler monostatic MIMO imaging method through adaptive estimation of the generalised Cauchy prior distribution (GCD). The superiority of GCD-based model over existing l p-norm-based model (which actually assumes the prior as general Gaussian distribution) is theoretically verified through the issue of signal compressibility. The authors adapt a reweighted l(2)-norm iterative algorithm to solve the model. In our model the authors do not pre-define the scaling factor of the prior distribution and, during the iteration, the authors use a novel 'quantile-of-OS' method to adaptively estimate the scaling parameter of the prior distribution, enhancing the robustness of the method. Simulation results verify the image quality and speed advantages of the proposed method.

关键词： adaptive estimation communication complexity Doppler radar Gaussian distribution radar imaging radar resolution MIMO radar image quality quantile-of-OS method â„“2-norm iterative algorithm signal compressibility â„“p-norm-based model GCD-based model generalised Cauchy prior distribution adaptive estimation range-angle-Doppler monostatic MIMO imaging method snapshots number high computational complexity poor robustness canonical MIMO imaging methods robust compressive multiinput-multioutput imaging

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Matrix inversion-less direct decoding for efficient channel estimation in fifth-generation massive MIMO systems

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IET COMMUNICATIONS 2020年第5期14卷 865-871页

作者： Zafar, Mohammad Haseeb Khan, Imran Basheri, Mohammed King Abdulaziz Univ Dept Informat Technol Jeddah Saudi Arabia Univ Engn & Technol Dept Elect Engn Peshawar Pakistan

Massive multiple-input-multiple-output (MIMO) is a fifth-generation wireless technology that equips the cellular base stations (BSs) with a very large number of antenna and is a communication technique of serving several users. Aiming at the problem of the high computational complexity of linear channel estimation and linear decoding in massive MIMO systems, this study proposes two low-complexity decoding algorithms according to the characteristics of the system. The fast decoding algorithm is designed by using the asymptotic orthogonality of the channel in the system to avoid solving the inverse of the channel matrix. In combination with increasing the number of base BS antennas to optimise user channel characteristics, a simple operator decoding algorithm is designed to realise direct decoding without estimating the channel. Simulation results show that the proposed algorithms can effectively reduce the computational complexity while ensuring system performance.

关键词： MIMO communication channel estimation decoding computational complexity matrix inversion-less efficient channel estimation fifth-generation massive MIMO systems multiple-input-multiple-output fifth-generation wireless technology cellular base stations communication technique high computational complexity linear channel estimation low-complexity decoding algorithms fast decoding algorithm channel matrix base BS antennas user channel characteristics simple operator decoding algorithm direct decoding system performance

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Efficient numerical analysis of finite FSS with multilayered media by MLACA

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IET MICROWAVES ANTENNAS & PROPAGATION 2019年第11期13卷 1804-1810页

作者： Hu, Yunqin Su, Ting Wan, Ting Nanjing Univ Posts & Telecommun Dept Commun Engn 66 Xinmofan Rd Nanjing Jiangsu Peoples R China Army Engn Univ PLA Nanjing Jiangsu Peoples R China

In this study, an efficient numerical technique for transmission characteristic analysis of finite planar frequency selective surface (FSS) with multilayered media is presented by using the multilevel adaptive cross approximation (MLACA). The computational scheme is based on the mixed-potential electric field integral equation (MPIE) with multilayered media Green's functions (MLGFs). The three-level modified discrete complex images method (DCIM) is presented for the spatial-domain MLGFs without any quasi-static and surface-wave extraction. For electrically large-size FSS structures, the MLACA method is then employed to alleviate the high computational complexity and memory requirement of method of moments. Numerical results are presented to demonstrate the accuracy and efficiency of the proposed method.

关键词： electric field integral equations frequency selective surfaces approximation theory method of moments numerical analysis Green's function methods finite FSS transmission characteristic analysis finite planar frequency selective surface multilevel adaptive cross approximation computational scheme mixed-potential electric field integral equation three-level modified discrete complex images method surface-wave extraction MLACA method high computational complexity numerical analysis numerical technique FSS structures spatial-domain MLGF multilayered media Green functions

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A reweighted matrix completion algorithm for sparse inverse synthetic aperture radar imaging

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IET RADAR SONAR AND NAVIGATION 2023年第1期17卷 38-46页

作者： Lv, Mingjiu Chen, Wenfeng Ma, Jianchao Yang, Jun Ma, Xiaoyan Cheng, Qi Air Force Early Warning Acad Radar NCO Sch Wuhan Peoples R China Air Force Early Warning Acad Early Warning Technol Wuhan Peoples R China

Sparse inverse synthetic aperture radar (ISAR) imaging can be generally achieved by compressed sensing (CS) methods because sparse sampling disables the approach of conventional range Doppler. However, the CS-based methods have to convert the matrix into a vector when the random sampling is adopted, which results in huge memory usage and high computational complexity. Note that matrix completion (MC) is suitable for matrix operations, which can recover random sparse data directly based on the low-rank property of the echo matrix. In order to improve the performance of the conventional MC approaches, a novel reweighted matrix completion method for sparse ISAR imaging is proposed in this paper. To avoid using the same singular value thresholding of the traditional MC method to achieve a low-rank solution, the reweighted scheme for singular values is applied to restrain the rank. Furthermore, the weights of the current iteration are updated with their singular values obtained in the former iteration, rather than using a fixed value. Then, the alternating direction method of multipliers is utilised to alternatively optimise the proposed model, which has an improved computational efficiency. Once the full data is recovered, the ISAR image can be achieved via the conventional 2D inverse fast Fourier transform. Experimental results based on measured data validate the proposed approach that can result in improving imaging performance within several seconds, which is tens of times faster than some reported low-rank-based methods.

关键词： image segmentation Optimisation techniques high computational complexity random sampling sparse inverse synthetic aperture radar imaging imaging performance conventional 2D inverse fast Fourier singular values Radar equipment, systems and applications novel reweighted matrix completion method convex programming iterative methods synthetic aperture radar singular value thresholding traditional MC method radar imaging Optical, image and video signal processing sparse sampling reweighted matrix completion algorithm sparse ISAR imaging compressed sensing random sparse data reweighted scheme compressed sensing methods reported low-rank-based methods matrix algebra Integral transforms in numerical analysis improved computational efficiency echo matrix Interpolation and function approximation (numerical analysis) conventional MC approaches conventional range Doppler CS-based methods ISAR image Linear algebra (numerical analysis) low-rank property matrix operations low-rank solution fast Fourier transforms huge memory usage computational complexity

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Hyperspectral image clustering via sparse dictionary-based anchored regression

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IET IMAGE PROCESSING 2019年第2期13卷 261-269页

作者： Huang, Nan Xiao, Liang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

Clustering for hyperspectral images (HSIs) is a very challenging task because HSIs usually have large spectral variability, high dimensionality, and complex structures. The main issue of this study is to develop an improved sparse subspace clustering (SSC) method for HSIs. As an extension of spectral clustering, SSC algorithm has achieved great success;however, the direct self-representation dictionary which is created by raw samples has poor representation power and also the widely used dictionary learning (DL) such as K-Singular Value Decomposition (K-SVD) faces with the problems of high computational complexity. In this study, the authors propose a novel HSI clustering method based on sparse DL and anchored regression. The proposed method follows three stages: (i) sparse DL;(ii) anchored subspace construction and regression;and (iii) representation-based spectral clustering. Specifically, we adopt a fast sparse DL method under a double sparsity constrained optimising model to capture the intrinsic HSIs. To establish a compact subspace for collaborative representation, we present an anchored subspace construction method by using atoms clustering and grouping methods. Owing to the anchored subspace, we can fast compute the representation coefficients with a predefined projection matrix. Experimental results demonstrate that the proposed method achieves the best performance for the HSIs clustering.

关键词： image representation hyperspectral imaging pattern clustering optimisation matrix algebra learning (artificial intelligence) sparse dictionary-based anchored regression hyperspectral images spectral variability high dimensionality complex structures improved sparse subspace clustering method SSC algorithm nature images low-dimensional data direct self-representation dictionary poor representation power high computational complexity representation-based spectral clustering fast sparse DL method intrinsic hyperspectral signatures compact subspace collaborative representation anchored subspace construction method hyperspectral data sets HSIs clustering task

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CMCS-net: image compressed sensing with convolutional measurement via DCNN

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IET IMAGE PROCESSING 2020年第15期14卷 3839-3850页

作者： Xie, Yahong Wang, Hailin Wang, Jianjun Southwest Univ Sch Math & Stat Chongqing Peoples R China Southwest Univ Inst Intelligent Finance & Digital Econ Chongqing Peoples R China

Recently, deep learning methods have made a remarkable improvement in compressed sensing image recovery stage. In the compressed measurement stage, the existing methods measured by block by block owing to a huge measurement dictionary for the whole images and the high computational complexity. In this work, a novel deep convolutional neural network (DCNN) named Convolutional Measurement Compressed Sensing network (CMCS-net) is proposed for image compressed sensing considering both convolutional measurement (CM) and sparse prior. Different from existing works, the convolution operation is adopted both in the measurement phase and reconstruction phase, which retains the structure information of images much better. Simultaneously, the size of the measurement matrix is no longer limited by data dimensions. Particularly, by unfolding the CM process to analyse a Toeplitz-type matrix, the theoretical support of the convolutional compressed measurement is proposed. In addition, in the recovery phase, the authors consider the sparse prior in nature images by embedding the truncated hierarchical projection algorithm into their architecture to solve the problem of multilayered convolutional sparse coding. Furthermore, extensive experiments demonstrate that their proposed CMCS-net can marvellously reconstruct the images and fully remove the block artefact.

关键词： image representation neural nets computational complexity image coding compressed sensing image reconstruction convolution data compression learning (artificial intelligence) CMCS-net image compressed sensing DCNN deep learning methods compressed sensing image recovery stage compressed measurement stage huge measurement dictionary high computational complexity deep convolutional neural network convolution operation measurement phase reconstruction phase measurement matrix convolutional compressed measurement recovery phase nature images multilayered convolutional sparse coding

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