Turbo Message Passing Based Burst Interference Cancellation for Data Detection in Massive MIMO-OFDM Systems

This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.

Vector Approximate Message Passing with Sparse Bayesian Learning for Gaussian Mixture Prior

Compressed sensing(CS)aims for seeking appropriate algorithms to recover a sparse vector from noisy linear observations.Currently,various Bayesian-based algorithms such as sparse Bayesian learning(SBL)and approximate message passing(AMP)based algorithms have been proposed.For SBL,it has accurate performance with robustness while its computational complexity is high due to matrix inversion.For AMP,its performance is guaranteed by the severe restriction of the measurement matrix,which limits its application in solving CS problem.To overcome the drawbacks of the above algorithms,in this paper,we present a low complexity algorithm for the single linear model that incorporates the vector AMP(VAMP)into the SBL structure with expectation maximization(EM).Specifically,we apply the variance auto-tuning into the VAMP to implement the E step in SBL,which decrease the iterations that require to converge compared with VAMP-EM algorithm when using a Gaussian mixture(GM)prior.Simulation results show that the proposed algorithm has better performance with high robustness under various cases of difficult measurement matrices.

一种基于Message Passing的并行程序设计技术

本文首先分析介绍构造并行计算机系统的两种方法：Ｍｕｌｔｉｐｒｏｃｅｓｓｏｒｓ和Ｍｕｌｔｉｃｏｍｐｕｔｅｒｓ，然后介绍一种Ｍｕｌｔｉｃｏｍｐｕｔｅｒｓ并行计算机系统中基于ｍｅｓｓａｇｅｐａｓｓｉｎｇ的并行程序设计环境以及并行程序设计方法，它具有程序设计简单、直观等特点，易于构造程序模型，并能获得较好的加速比。

基于Message Passing的并行编程环境

在分布式并行计算机系统中，由于处理机间无共事内存，因此采用了MessagePassins方式实现处理机间的通信.文中讨论了基于MessagePassing把的并行编程环境所应具备的特点，然后介绍几种被人们普遍接受的并行编程环境.

一种基于Message Passing的并行程序设计环境

并行处理技术的发展使得高性能的并行计算机系统不断地推出，而方便、灵活的并行程序设计环境在并行计算机系统的推广应用中起着重要的作用。如何编写有效的并行程序代码以及如何将现有的串行应用程序并行化已成为并行处理的重要研究课题。

基于Message Passing的认知无线电快速资源分配

针对多用户的OFDM认知无线电系统上行链路,提出一种基于Message Passing的分布式快速资源分配算法。该算法以认知系统总发射功率最小化为优化目标,综合考虑了认知用户对授权用户的干扰、总发射功率预算以及认知用户之间的比例公平性等约束条件,将资源分配分为子信道分配与功率分配相继2个步骤,构建资源分配的因子图,通过在节点间迭代地传递信息直至最终完成分布式的资源分配。分析和仿真结果表明,该算法在保证系统通信性能及资源分配公平性的前提下降低了系统总发射功率,并且运算效率得到了明显提升。

QIM digital watermarkingbased on LDPC code and messagepassingunder scalingattacks

Watermarking system based on quantization index modulation （QIM） is increasingly popular in high payload applications,but it is inherently fragile against amplitude scaling attacks.In order to resist desynchronization attacks of QIM digital watermarking,a low density parity check （LDPC） code-aided QIM watermarking algorithm is proposed,and the performance of QIM watermarking system can be improved by incorporating LDPC code with message passing estimation/detection framework.Using the theory of iterative estimation and decoding,the watermark signal is decoded by the proposed algorithm through iterative estimation of amplitude scaling parameters and decoding of watermark.The performance of the proposed algorithm is closer to the dirty paper Shannon limit than that of repetition code aided algorithm when the algorithm is attacked by the additive white Gaussian noise.For constant amplitude scaling attacks,the proposed algorithm can obtain the accurate estimation of amplitude scaling parameters.The simulation result shows that the algorithm can obtain similar performance compared to the algorithm without desynchronization.

Efficient Recovery of Structured Sparse Signals via Approximate Message Passing with Structured Spike and Slab Prior

Due to limited volume, weight and power consumption, micro-satellite has to reduce data transmission and storage capacity by image compression when performs earth observation missions. However, the quality of images may be unsatisfied. This paper considers the problem of recovering sparse signals by exploiting their unknown sparsity pattern. To model structured sparsity, the prior correlation of the support is encoded by imposing a transformed Gaussian process on the spike and slab probabilities. Then, an efficient approximate message-passing algorithm with structured spike and slab prior is derived for posterior inference, which, combined with a fast direct method, reduces the computational complexity significantly. Further, a unified scheme is developed to learn the hyperparameters using expectation maximization(EM) and Bethe free energy optimization. Simulation results on both synthetic and real data demonstrate the superiority of the proposed algorithm.

Speech Enhancement Based on Approximate Message Passing

To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector(VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passing(AMP) is adopted. AMP exploits the difference between speech and noise sparsity to remove or mute the noise from the corrupted speech. The AMP algorithm is adopted to reconstruct the clean speech efficiently for speech enhancement. More specifically, the prior probability distribution of speech sparsity coefficient is characterized by Gaussian-model, and the hyper-parameters of the prior model are excellently learned by expectation maximization(EM) algorithm. We utilize the k-nearest neighbor(k-NN) algorithm to learn the sparsity with the fact that the speech coefficients between adjacent frames are correlated. In addition, computational simulations are used to validate the proposed algorithm, which achieves better speech enhancement performance than other four baseline methods-Wiener filtering, subspace pursuit(SP), distributed sparsity adaptive matching pursuit(DSAMP), and expectation-maximization Gaussian-model approximate message passing(EM-GAMP) under different compression ratios and a wide range of signal to noise ratios(SNRs).

Parametric message passing-based relative navigation in joint tactical information distribution system

Relative navigation is a key feature in the joint tactical information distribution system（JTIDS）.A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.First of all,the joint posterior distribution of all the terminals＇ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival（TOA） measurement,messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end,the Euclidean norm is approximated by Taylor expansion.Then,all the messages on the factor graph can be derived in Gaussian forms,which enables the terminals to transmit means and covariances.Finally,the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations,e.g.,range measurement noise,priors of position uncertainty and velocity noise.Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS.

Thread-Oriented Message-Passing Interface

In this paper the limitations of the standard Message Passing Interface (MPI) are analyzed, then the technique of multithreading is introduced and a stand alone LWP (light weight process) extension to MPI is implemented. Experimental results show that system overhead is considerably diminished.

Message Passing Based Detection for Orthogonal Time Frequency Space Modulation

The orthogonal time frequency space(OTFS)modulation has emerged as a promis⁃ing modulation scheme for wireless communications in high-mobility scenarios.An efficient detector is of paramount importance to harvesting the time and frequency diversities promised by OTFS.Recently,some message passing based detectors have been developed by exploiting the features of the OTFS channel matrices.In this paper,we provide an overview of some re⁃cent message passing based OTFS detectors,compare their performance,and shed some light on potential research on the design of message passing based OTFS receivers.

Combined UAMP and MF Message Passing Algorithm for Multi-Target Wideband DOA Estimation with Dirichlet Process Prior

When estimating the direction of arrival (DOA) of wideband signals from multiple sources, the performance of sparse Bayesian methods is influenced by the frequency bands occupied by signals in different directions. This is particularly true when multiple signal frequency bands overlap. Message passing algorithms (MPA) with Dirichlet process (DP) prior can be employed in a sparse Bayesian learning (SBL) framework with high precision. However, existing methods suffer from either high complexity or low precision. To address this, we propose a low-complexity DOA estimation algorithm based on a factor graph. This approach introduces two strong constraints via a stretching transformation of the factor graph. The first constraint separates the observation from the DP prior, enabling the application of the unitary approximate message passing (UAMP) algorithm for simplified inference and mitigation of divergence issues. The second constraint compensates for the deviation in estimation angle caused by the grid mismatch problem. Compared to state-of-the-art algorithms, our proposed method offers higher estimation accuracy and lower complexity.

MPI:大有希望的massage_passing标准

message-passing是一个被广泛应用的并行计算模型,但至今,无论从语法或语义上message-Passing都还没有形成一个标准.并行计算机厂商在提供标准串行语言的同时,提供了各自不同的专有message一passing库,并行程序设计难免依赖于具体的系统和应用,这既限制了可移植性,也限制了听取用户对某些不易获得或对当前的应用不合适的系统的意见。没有一个标准,厂商们非常自然地把他们的专有库的优点视为有竟争力的优势,更加着力与众不同(因而不可移植)。为了应付可移植问题,研究团体已经开发了许多库来收集其它库之间的不同点.

注意力感知的边−节点交换图神经网络模型

提出了一种注意力感知的边-节点交换图神经网络(attention aware edge-node exchange graph neural network,AENN)模型,在图结构化数据表示框架下,使用边-节点切换卷积的图神经网络算法进行图编码,用于半监督分类和回归分析。AENN是一种通用的图编码框架,用于将图节点和边嵌入一个统一的潜在特征空间。具体地,基于原始无向图,不断切换边与节点的卷积,并在卷积过程中通过注意力机制分配不同邻居的权重,从而实现特征传播。在3个数据集上的实验研究表明,所提方法较已有方法在半监督分类和回归分析中具有明显的性能提升。

基于DeFi消息传递机制的跨链交易关联方法

跨链桥作为一种新型区块链基本服务,实现了区块链间的资产和数据流通,极大地促进了区块链去中心化金融(DeFi)的建设。然而在实际应用过程中,由于跨链桥的交易不透明与资产难追踪问题,许多犯罪分子开始将跨链技术作为一种洗钱手段,在链间进行非法资产的转移。因此,跨链交易追踪问题正逐渐成为学术界与相关监管部门关注的焦点。现有方法主要通过规则匹配的启发式算法来实现跨链交易的关联,这种方法虽能有效匹配特定区块链间的交易,但仅面向中心化金融(CeFi)跨链桥服务,且高度依赖于中心化的内部应用程序编程接口(API),难以广泛支持多种跨链桥的交易关联。为解决以上问题,提出一种面向DeFi跨链桥服务的交易关联方法。根据跨链桥消息传递机制获取源链的存款事件日志,并基于专家规则提取关键跨链信息,利用数据爬取技术构造目标链交易搜索空间。在此基础上,通过多种业务规则实现跨链交易的关联。实验结果表明,该方法在Celer cBridge、Multichain和Poly Network这3座跨链桥的真实交易数据集(以太坊为源链,币安智能链为目标链)上准确率分别达到93.6%、95.3%和91.6%,具有较好的关联效果。

基于近似消息传递的NOMA系统信道和脉冲噪声联合估计方法

针对非高斯脉冲噪声背景下的非正交多址接入(non-orthogonal multiple access,NOMA)系统的信道估计问题,利用信道和脉冲噪声的稀疏特性,提出一种基于近似消息传递的信道和脉冲噪声联合估计方法。首先构建全子载波的压缩感知方程,然后基于稀疏贝叶斯学习理论提出一种信道、脉冲噪声和数据符号的联合估计优化问题。为解决这一超参量非线性非凸问题,设计了一种基于高斯广义近似消息传递和稀疏贝叶斯学习理论的期望最大化实现算法。仿真结果表明,与基于期望最大化的稀疏贝叶斯学习方法相比,所提算法在信道和脉冲噪声估计的均方误差、误码率等方面性能虽略有下降,但算法复杂度降低了1个数量级。

基于聚类与稀疏字典学习的近似消息传递

基于传统字典学习的近似消息传递(approximate message passing,AMP)算法对训练样本数量的需求较高,且运算成本较高。本文引入双稀疏模型,构建基于稀疏字典学习的AMP框架,降低迭代过程中字典学习对训练样本数量的需求,提高压缩感知图像重建的质量与效率。进一步,提出基于聚类与稀疏字典学习的AMP算法,在迭代过程中依据图像块特征进行分类,并为各类图像块分别学习稀疏字典,实现自适应去噪。与基于传统字典学习的AMP算法相比,基于聚类与稀疏字典学习的AMP算法能够将重建图像的峰值信噪比提高0.20~1.75 dB,并且能够将运算效率平均提高89%。

可重构智能表面辅助的无源波束成形和信号检测

针对基于空间调制的可重构智能表面(Reconfigurable Intelligent Surface, RIS)联合无源波束成形和信号检测系统中无源波束成形的非凸二次约束二次规划问题,提出了基于连续凸逼近(Successive Convex Approximation, SCA)和优化最小化(Majorization-Minimization, MM)的低复杂度算法。针对用户信息的双线性检测问题,提出自适应阻尼因子双线性广义近似消息传递(Bilinear Generalized Approximate Message Passing, BiG-AMP)算法,在保证误码率性能的同时算法具有更快的收敛速度与更低的复杂度。针对仅有0和1组成的可重构智能表面信息检测,提出基于广义近似消息传递(Generalized Approximate Message Passing, GAMP)算法的改进算法,在计算从检查节点到变量节点的消息时算法不使用GAMP算法中近似值,而直接计算信息。仿真结果表明,在降低误码率性能方面改进的GAMP算法比GAMP实现了4 dB的信噪比增益。

基于CGA的MPI程序分支覆盖测试套件生成

针对程序的分支覆盖测试,元启发式搜索技术已经被广泛应用于测试数据生成中。然而,当前的研究成果主要适用于串行程序。因此,为覆盖消息传递接口(Message Passing Interface,MPI)程序的分支,该文研究基于协同进化遗传算法(Co-evolutionary Genetic Algorithm,CGA)的测试套件生成方法(简称为:CGA生成法),该方法具有不受不可行分支影响的优势。首先,基于收集覆盖信息的探针,定义最小归一化分支距离,并以此设计出相应的适应度值函数;然后,使用CGA生成进化个体,并基于设计的适应度值函数,计算这些个体的适应值;最后,基于计算的适应值,选择子种群中代表个体,以构成合作种群。所提CGA生成法应用于7个基准MPI程序,并与其他多种方法进行比较。实验结果表明,CGA生成法的覆盖率通常高于其他搜索算法。