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Radio Map Assisted Path Planning for UAV Anti-Jamming Communications

IEEE signal processing LETTERS 2022年 29卷 607-611页

作者： Dong, Yangrui He, Chen Wang, Ziteng Zhang, Lin Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China

The communications quality of unmanned aerial vehicle (UAV) has been brought into focus increasingly, which can be severely affected by other users, as well as complex topography. In this letter, we propose a path planning algorithm for UAV anti-jamming communications with the joint assistance of two kinds of radio maps-signal power map and signal to interference plus noise ratio (SINR) map, which contain the abundant information of path loss caused by the topography and interference from jammers. We show how to generate these maps from real topographic data, and provide the optimal UAV path planning by adopting the power and the SNR maps to the heuristic and graphic based A* algorithm. Simulations with various settings for areas with complex topography in real world, show that the proposed algorithm can considerably improve the efficiency by minimizing the UAV flying distance for anti-jamming communications.

关键词： Interference signal to noise ratio Autonomous aerial vehicles Path planning Jamming signal processing algorithms Base stations UAV path planning anti-jamming communi-cation radio map

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Convergence Rate Comparison of Proximal algorithms for Non-Smooth Convex Optimization With an Application to Texture Segmentation

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IEEE signal processing LETTERS 2022年 29卷 1337-1341页

作者： Briceno-Arias, L. M. Pustelnik, N. Univ Tecn Federico Santa Maria Dept Math Santiago 8940897 Chile Univ Lyon Ens Lyon Univ Lyon 1 Lab PhysCNRS Lyon France UCLouvain ICTEAM Inst ISPGrp INMA B-1348 Louvain La Neuve Belgium

In this paper we provide a theoretical and numerical comparison of convergence rates of forward-backward, Douglas-Rachford, and Peaceman-Rachford algorithms for minimizing the sum of a convex proper lower semicontinuous function and a strongly convex differentiable function with Lipschitz continuous gradient. Our results extend the comparison made in [1], when both functions are smooth, to the context where only one is assumed differentiable. Optimal step-sizes and rates of the three algorithms are compared theoretically and numerically in the context of texture segmentation problem, obtaining very sharp estimations and illustrating the high efficiency of Peaceman-Rachford splitting.

关键词： Convergence signal processing algorithms Task analysis Optimization Linear programming Fractals Convex functions Convex optimization convergence rates proximal algorithms texture segmentation

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DualPRNet: Deep Shrinkage Dual Frame Network for Deep Unrolled Phase Retrieval

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IEEE signal processing LETTERS 2022年 29卷 1177-1181页

作者： Shi BaoShun Lian QiuSheng Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China

Phase retrieval (PR), i.e., the recovery of the underlying image from the measurements without phase information, is a challenging task, especially at low signal to noise ratios (SNRs). Recent deep unrolling optimizations of tackling this task offer both computational efficiency and high-quality reconstructions. In this work, we involve a novel deep shrinkage network (DSN) into the supervised dual frame learning framework, and propose a deep shrinkage dual frame network dubbed as DualNet for building a deep unrolled PR network architecture. Traditional thresholding functions with hand-crafted thresholds for filtering the frame coefficients are non-adaptive, which limits the final reconstruction quality. Instead, we elaborate a DSN that can learn instance-adaptive and spatial-variant thresholding functions. In a nutshell, we propose the so-called DualPRNet by incorporating the learned dual frames into the unrolled PR framework. Experiments demonstrate that DualPRNet can achieve higher-quality reconstructions compared with previous PR iteration algorithms at low SNRs.

关键词： Image reconstruction Thresholding (Imaging) Supervised learning Imaging Training Task analysis signal processing algorithms Deep shrinkage network deep unrolling dual frames phase retrieval supervised learning

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Steady-State Mean-Square Error Analysis for Adaptive Joint Process Filter Using Pipelined Feedforward Second-Order Volterra Architecture

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1957-1961页

作者： Liu, Qianqian He, Yigang Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China

To lower the high computational burden of nonlinear filter in the practical applications, the previous paper has proposed the adaptive algorithm based on the joint process filter using pipelined feedforward second-order Volterra architecture (JPPSOV). Even though the JPPSOV adaptive filter has been common used for nonlinear systems identification, nonlinear active noise controller (ANC) and nonlinear acoustic echo cancellers (AEC), there is currently no literature that gives the steady state analysis of JPPSOV. Therefore, we analyze the steady state performance of the LMS adaptive algorithm of the JPPSOV filter for nonlinear system identification in this brief. Finally, the simulated results are in good agreement with the theoretical results.

关键词： Steady-state Pipelines Computer architecture signal processing algorithms Nonlinear systems Circuits and systems Adaptation models Volterra filter pipelined architecture steady-state analysis nonlinear filter

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Proximal Based Strategies for Solving Discrete Mumford-Shah With Ambrosio-Tortorelli Penalization on Edges

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IEEE signal processing LETTERS 2022年 29卷 952-956页

作者： Le, Hoang Trieu Vy Foare, Marion Pustelnik, Nelly Univ Lyon Lab Phys CNRS Ens Lyon F-69342 Lyon France Univ Lyon France CNRS Ens Lyon INRIA Lyon France CPE Lyon Lyon France UC Louvain ISPGrp ICTEAM Louvain Belgium

This work is dedicated to joint image restoration and contour detection considering the Ambrosio-Tortorelli functional. Two proximal alternating minimization schemes with convergence guarantees are provided, PALM-AT and SL-PAM-AT, as well as closed-form expressions of the involved proximity operators. A thorough numerical study is conducted in order to evaluate the performance of both numerical schemes as well as comparisons to state-of-the-art Mumford-Shah strategies.

关键词： Image restoration Image edge detection signal processing algorithms Minimization Convergence TV Degradation Image restoration edge detection Mumford-Shah Ambrosio-Tortorelli proximal algorithms

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An Improved Constrained LMS Algorithm for Fast Adaptive Beamforming Based on a Low Rank Approximation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第8期69卷 3605-3609页

作者： Vadhvana, Sanket Yadav, Shekhar Kumar Bhattacharjee, Sankha Subhra George, Nithin V. Indian Inst Technol Gandhinagar Dept Elect Engn Gandhinagar 382355 India

Adaptive beamformers use data from sensor arrays to capture signal from a desired direction without any distortion, in the presence of interfering signals from other directions in a noisy environment. Most beamformers achieve this goal by minimizing their variance while applying distortionless and null constraints in the direction of the desired and interfering signals, respectively. Constrained least-mean-square (CLMS) algorithms have been developed to iteratively update the weights of such beamformers. In this brief, we propose a novel and improved CLMS beamforming algorithm based on a low rank approximation technique called the nearest Kronecker product decomposition. By decomposing the weight vector into a sequence of Kronecker products of smaller vectors, the original weight update process is converted into updates of smaller vectors. The decomposition allows us to control the trade-off between steady-state performance and faster convergence based on the rank of the beamforming system. We derive the update rules of the proposed algorithm, tabulate its computational complexity and perform simulation study to show its superiority.

关键词： Array signal processing signal processing algorithms Steady-state Circuits and systems Sensor arrays Convergence Optimization Adaptive beamforming nearest Kronecker product constrained LMS sensor array signal processing

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Semi-Blind Channel Estimation in MIMO Systems With Discrete Priors on Data Symbols

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IEEE signal processing LETTERS 2022年 29卷 51-54页

作者： Al-Shoukairi, Maher Rao, Bhaskar D. Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92122 USA

In this work, we addressthe MIMO semi-blindchannel estimation problem. We propose an eigenvalue decomposition based technique to significantly reduce the dimensionality of the EM based algorithm when the imposed prior on the data is Gaussian, greatly lowering the computational complexity. In addition to that, we apply the Minimum Power Distortionless Response (MPDR) decoupling principle to derive a tractable EM algorithm that uses the actual discrete prior of the data symbols. Our results show that the proposed MPDR based algorithm has superior performance over other EM based algorithms in both low and high SNR regions. The results also show that a faster version of the algorithm can be obtained by initializing it using the eigenvalue decomposition based Gaussian algorithm.

关键词： Channel estimation Covariance matrices Complexity theory signal processing algorithms MIMO communication signal to noise ratio Maximum likelihood estimation Channel estimation semi-blind EM algorithm massive MIMO MPDR decoupling discrete priors

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Energy Efficiency Optimization of Reconfigurable Intelligent Surfaces With Electromagnetic Field Exposure Constraints

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IEEE signal processing LETTERS 2022年 29卷 1447-1451页

作者： Zappone, Alessio Di Renzo, Marco Univ Cassino & Southern Lazio I-03043 Cassino Italy Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91192 Gif Sur Yvette France

This work considers the problem of energy efficiency (EE) maximization in a reconfigurable intelligent surface (RIS)-aided multiple input multiple output (MIMO) communication link, subject to maximum power constraints and to additional constraints on the maximum exposure of the end-users to electromagnetic radiations. The RIS phase shifts, the transmit beamforming, the linear receive filter, and the transmit power are jointly optimized, and two provably convergent and low-complexity algorithms are developed. One algorithm applies to the general system setup, but does not guarantee global optimality. The other is provably optimal in the notable special case of isotropic electromagnetic field (EMF) exposure constraints. The numerical results show that an RIS ensures an EE of the same order of magnitude as when no EMF constraints are enforced.

关键词： signal processing algorithms Optimization Transmitting antennas Array signal processing Wireless communication Receiving antennas Radio transmitters RIS energy efficiency EMF exposure resource allocation

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Fitting Nonlinear signal Models Using the Increasing-Data Criterion

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IEEE signal processing LETTERS 2022年 29卷 1302-1306页

作者： Li, Jimei Ding, Feng Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China

To extract important information about the nonlinear signals, this letter makes the utmost of the fitting advantages of Gaussian and polynomial functions, and proposes a nonlinear signal model with broader applications. Then we focus on the parameter estimation issues of the proposed models in the presence of noises. The stability factor recursive algorithm is devised based on the increasing noisy data, which makes full use of the information from the nonlinear signals. Applying the hierarchical identification principle, a two-stage recursive algorithm with higher computational efficiency is developed for the nonlinear signals. The simulation results test the effectiveness of the proposed algorithms from the aspects of estimation accuracy and prediction effect.

关键词： signal processing algorithms Estimation Parameter estimation Approximation algorithms Stability criteria Prediction algorithms Noise measurement Function approximation nonlinear signal para- meter estimation recursive search hierarchical identification

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Optimal Penalty Factor for the MOV-FxLMS Algorithm in Active Noise Control System

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IEEE signal processing LETTERS 2022年 29卷 85-89页

作者： Shi, Dongyuan Gan, Woon-Seng Lam, Bhan Shen, Xiaoyi Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The minimum output variance filtered reference least mean square (MOV-FxLMS) algorithm is a effective algorithm that utilizes the penalty mechanism to help the active noise control (ANC) system achieve noise cancellation with constrained output variance or power. As it can constrain output power, the MOV-FxLMS algorithm can freely determine the ANC system's control effort, avoiding output saturation, and improving system stability. However, its performance is determined by a penalty factor, which is normally chosen by trial and error. Hence, this work proposes an optimal penalty factor and its feasible estimation that does not require any assumptions of Gaussian reference signal or input independence. This factor assists the MOV-FxLMS in achieving the optimal solution under the target output-variance constraint. Numerical simulations on measured paths demonstrate its effectiveness for various types of noise.

关键词： signal processing algorithms Filtering algorithms Adaptive filters Power generation Optimal control Power amplifiers Noise reduction Active noise control (ANC) output constraint minimum output variance (MOV) filtered reference least mean squares (FxLMS) algorithm

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