基于高斯树近似的大规模MIMO检测算法

大规模MIMO系统中,基站端复杂的接收信号将导致信号检测困难等问题。该文提出一种基于截断诺依曼级数近似求逆高斯树的低复杂度消息传递算法,该算法在高斯树近似消息传递的过程中利用诺依曼级数近似求解协方差矩阵和最小均方误差估计以降低计算复杂度。仿真结果表明,该文算法在检测性能满足系统需求的同时,也有较低的计算复杂度。     

基于提前终止迭代的概率近似消息传递检测算法

大规模多输入多输出技术作为第5代通信系统的关键技术,可有效提高频谱利用率。基站端采用消息传递检测(MPD)算法可以实现良好的检测性能。但是由于MPD算法的计算复杂度随调制阶数和用户天线数的增加而增加,而概率近似消息传递检测(PA-MPD)算法可以减少MPD算法的计算复杂度。为了进一步降低PA-MPD算法的复杂度,该文在PA-MPD算法的基础上引入了提前终止迭代策略,提出了一种改进的概率近似消息传递检测算法(IPA-MPD)。首先确定不同用户的符号概率在迭代过程中的收敛速率,然后根据收敛率来判断用户的符号概率是否达到最佳收敛,最后对符号概率到达最佳收敛的用户终止算法迭代。仿真结果表明,在不同单天线用户配置下IPA-MPD算法的计算复杂度可降低为PA-MPD算法的52%～77%,且不损失算法的检测性能。     

无线网络中基于变分消息传递的分布式协作定位算法

针对基于消息传递算法的节点定位方法复杂度和通信开销较高的问题,提出一种适用于节点可移动网络的低复杂度低协作开销的节点自定位算法。为降低通信负载,该算法将消息约束为高斯型函数,网络中只需传输各消息的均值和方差,并采用适用于指数模型的变分消息传递(VMP)算法以降低计算复杂度。首先,根据节点的历史轨迹对节点位置进行预测,得到当前时刻的先验信息。然后,在因子图上按照VMP消息更新规则、通过迭代近似求解节点位置变量的后验分布。在消息更新中,对于非线性测距模型引起的非高斯置信,通过非线性项的二阶泰勒级数展开将其近似为高斯型函数。最后,根据最大后验估计准则得到位置估计。仿真结果表明,该算法的定位精度与基于非参数化置信传播的SPAWN（Sum-Product Algorithm over a Wireless Network）接近,但计算复杂度和通信负载均显著降低。      

基于广义近似消息传递的扰码多址卫星接收技术

为了解决卫星互联网中各类终端设备对传输速率和频谱资源的需求剧增问题,针对非正交多址卫星互联网,设计了一种基于广义近似消息传递的优化卫星接收机。考虑非正交多址中的多用户检测,基于图模型建立因子图,利用因子图建立系统的消息传递模型并提出消息传递算法。基于广义近似消息传递算法,进一步降低传统基于消息传递的多用户检测器的复杂度。在卫星环境下,研究广义近似消息传递信号接收性能。仿真结果表明,所提算法能有效地解决星地之间的多用户信号共存问题,同时降低算法的运算复杂度。     

面向6G超大规模MIMO系统分布式基带处理信号检测算法

为缓解现有大规模MIMO系统集中式基带处理架构带来的带宽和计算瓶颈,DBP架构被提出.面向6G超大规模MIMO系统,提出一种DBP架构下基于EP算法的分布式信号检测算法,通过引入符号置信度构建消息传递规则并利用高斯函数近似降低消息传递算法计算复杂度,根据因子图设计中心处理单元中的消息混合规则.仿真结果表明,所提分布式E...     

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-inimization,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睞MP)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LDPC编码的MIM0系统迭代接收机研究

研究了基于LDPC编码无线MIMO通信系统中的软输出最小均方误差干扰抵消迭代(MMSE PIC)检测算法.针对初次迭代检测时PIC输出的后验比特对数似然比(LLR)不可靠的问题,利用MMSE滤波器输出的高斯近似表示,给出了基于后验概率估计的迭代干扰抵消检测算法,以提高检测器输出的编码比特对数似然比的可靠性.仿真结果表明:改进的检测算法优于现有算法.     

OTFS调制系统的低复杂度GAMP算法实现

针对正交时频空（Orthogonal Time Frequealy Space,OTFS）调制技术信号检测算法复杂度高的问题,结合OTFS信道矩阵特性构建匹配滤波器,在广义近似消息传递(Generalized Approximate Message Passing,GAMP)算法的基础上,提出了基于期望最大化(Empectation Maxinization,EM)的阻尼广义消息传递算法。相较于传统的消息传递（Message Passing,MP）、近似消息传递（Approximate Message Passing,AMP）和GAMP等检测方式,所提算法有更低复杂度。在500 km/h的高动态场景中对该算法进行仿真,其误码率性能优于最小均方差（Minimum Mean Squared Error,MMSE）和传统的GAMP检测算法,并且具有更好的收敛性。     

基于重加权近似消息传递算法的稀疏信道估计

基于近似消息传递(AMP)算法,提出了一种重加权近似消息传递(Rw AMP)算法用于稀疏信道估计,该算法增加了重加权过程与衰减机制。在信号重构时加入权重更新过程,待重构信号中较大的元素几乎不变,较小元素迅速降低为零,从而提高正确重构概率。其次,当稀疏信道估计的观测矩阵为Toeplitz矩阵时,为了提高AMP类算法收敛性,文中加入衰减机制。仿真结果表明,在相同复杂度的条件下,Rw AMP算法的信号重构性能优于基追踪(BP)算法和AMP算法。     

高阶QAM调制MIMO系统高斯逼近简化算法

针对多输入多输出(MIMO)高阶正交幅度调制(QAM)系统检测算法使用传统的高斯逼近算法检测复杂度高的问题,研究了其简化算法.MIMO系统高斯逼近算法通过采用树形搜索的办法,对符号后验概率影响大的符号序列加以保留,其算法复杂度与保留符号序列数目成线性,且与调制阶数成指数关系.针对高阶调制QAM符号的特点,用多级映射的方法来进行高斯逼近,新方法结合树形搜索算法的思想,通过将高阶调制QAM符号拆分成多个4-QAM符号,从而可以每次两个比特的计算序列权重,其算法复杂度与调制阶数成线性关系,从而使得高阶调制系统的检测复杂度大大降低.仿真结果表明,多级映射高斯逼近算法能取得接近传统高斯逼近算法的性能.     

A New Modified Gaussian Approximation Detector for Space-Time Bit-Interleaved Coded Modulation Schemes

To reduce the receiver complexity of the space-time bit-interleaved coded modulation (ST-BICM) scheme, this letter describes a turbo-type iterative structure consisting of a modified Gaussian approximation (MGA) detector and an a posteriori probability (APP) decoder. Utilizing the characteristics of the interleaving and the central limited theoretic, the MGA detector first assumes the superposition of the transmitted signals as a Gaussian random variable. Then, P most significant signal combinations are identified to calculate the log-likelihood ratio of each bit. Without any pre-process on the received signals, the proposed method is free of matrix inverse operation and can be applied in systems with more transmit antennas than receive antennas. Furthermore, the performance of the MGA approaches that of the optimal detection     

Soft-Output Decoding of Rotationally Invariant Codes Over Channels With Phase Noise

We consider rotationally invariant (RI) trellis-coded modulations (TCMs) transmitted over channels affected by phase noise. To describe the main ideas of this paper, we first concentrate, as a case study, on the simplest RI scheme, namely the differentially encoded M-ary phase-shift keying (M-PSK) signal. For this problem, we use the framework based on factor graphs (FGs) and the sum-product algorithm (SPA), to derive the exact maximum a posteriori (MAP) symbol detection algorithm. By analyzing its properties, we demonstrate that it can be implemented by a forward-backward estimator of the phase probability density function, followed by a symbol-by-symbol completion to produce the a posteriori probabilities of the information symbols. To practically implement the forward-backward phase estimator, we propose a couple of schemes with different complexity. The resulting algorithms exhibit an excellent performance and, in one case, only a limited complexity increases with respect to the algorithm that perfectly knows the channel phase. The properties of the optimal decoder and the proposed practical decoding schemes are then extended to the case of a generic RI code. The proposed soft-output algorithms can also be used in iterative decoding schemes for concatenated codes employing RI inner components. Among them, in the numerical results, we consider repeat-accumulate (RA) codes and other serially concatenated schemes recently proposed in the technical literature.     

A low-complexity sequential Monte Carlo algorithm for blind detection in MIMO systems

In statistical signal processing, the sequential Monte Carlo (SMC) method is powerful and can approach the theoretical optima. However, its computational complexity is usually very high, especially in multiple-input multiple-output (MIMO) systems. This paper presents a new low-complexity SMC (LC-SMC) algorithm for blind detection in MIMO systems, the main idea of which is to shrink the sampling space via channel estimation which is initialized using the first differentially modulated symbol and then updated using the Monte Carlo samples. Since the a posteriori probability of the transmitted symbols can be calculated separately by each transmit antenna, the proposed LC-SMC algorithm is not only computationally efficient, as compared to the original SMC whose complexity grows exponentially with the number of transmit antennas, but also makes blind turbo receiver more feasible for multilevel/phase modulations. Simulation results are presented to demonstrate the effectiveness of the LC-SMC algorithm.     

Improved VBLAST MAP: A Novel Point-to-Point Symbol Detection Algorithm for MIMO Wireless Communication Systems

Anovel point-to-point symbol detection algorithm in multiple input multiple output (MIMO) system is proposed. This algorithm is an augmentation of two popular algorithms, namely, vertical Bell laboratories layered space-time (VBLAST) and maximum a posteriori probability (MAP). Here, layers are distinguished or ordered based on the a posteriori probabilities of output symbols and not on signal-to-noise ratio (SNR). For each layer, a set of a posteriori probabilities is computed for all output symbols using all possible signal constellations. The layer corresponding to the output symbol having minimum a posteriori probability is selected first from the set of a posteriori probabilities for detection by doing the comprehensive search over all the possible signal constellations. Then, the remaining layers are detected by the conventional VBLAST MAP technique. The relationship of MIMO symbol error rate (MIMO SER) versus MIMO symbol SNR is presented using simulations for 16×16 MIMO systems and 16-QAM constellation. The results show that the proposed algorithm outperforms conventional VBLAST MAP and improved VBLAST algorithms.     

HTRCI and Channel Ranking Based Joint Symbols Detection for MQRD-PCM/MIMO-OFDM

MIMO-OFDM is considered a key technology in emerging high-data rate systems. In MIMO-OFDM systems, channel estimation and signal detection are important to distinguish transmit signals from multiple transmit antennas. Previously, we have proposed a parallel detection algorithm using multiple QR decompositions (Q is an orthogonal square matrix, R is upper triangular matrix) with permuted channel matrix (MQRD-PCM) to reduce the system complexity of MIMO-OFDM. This method achieves a good BER performance with a low system complexity. However, since MQRD-PCM is a kind of parallel detection method, the wrong detection probability is increased due to the bad channel signal-to-interference plus noise power ratio (SINR) of the transmitted signals. As a result, the average BER performance is influenced by the wrong detection probability of the bad channel SINR. To overcome the above-mentioned problems, in this paper, we propose the high time resolution carrier interferometry and channel ranking based joint signal detection for MQRD-PCM/MIMO-OFDM.     

基于非参量CUSUM的认知超宽带频谱感知算法

提出了一种基于非参量CUSUM的超宽带信号快速检测算法,该算法针对认知超宽带频谱感知中超宽带信号检测问题,根据超宽带信号占空比低的特点,选择短时能量作为非参量CUSUM算法的观测统计量,将块检测思想和序列检测思想进行有机结合,克服了块检测算法的信噪比门限效应,并采用加滑动预处理窗的方式降低噪声对观测统计量的影响。本文通过自适应改变非参量CUSUM算法中Reference Value取值的方法,提出改进算法以进一步缩短检测延迟。仿真证明,占空比为1/10、信噪比为-25dB时, 所提算法可在用户发送50个脉冲的时间内以90%的概率检测出主用户信号的存在,而能量检测算法在8dB时才能达到相同检测概率,且所提算法检测延迟小于能量检测算法。改进算法在-10dB≤SNR≤5dB条件下可明显缩短检测延迟。      

On the application of factor graphs and the sum-product algorithm to ISI channels

In this paper, based on the application of the sum-product (SP) algorithm to factor graphs (FGs) representing the joint a posteriori probability (APP) of the transmitted symbols, we propose new iterative soft-input soft-output (SISO) detection schemes for intersymbol interference (ISI) channels. We have verified by computer simulations that the SP algorithm converges to a good approximation of the exact marginal APPs of the transmitted symbols if the FG has girth at least 6. For ISI channels whose corresponding FG has girth 4, the application of a stretching technique allows us to obtain an equivalent girth-6 graph. For sparse ISI channels, the proposed algorithms have advantages in terms of complexity over optimal detection schemes based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. They also allow a parallel implementation of the receiver and the possibility of a more efficient complexity reduction. The application to joint detection and decoding of low-density parity-check (LDPC) codes is also considered and results are shown for some partial-response magnetic channels. Also in these cases, we show that the proposed algorithms have a limited performance loss with respect to that can be obtained when the optimal "serial" BCJR algorithm is used for detection. Therefore, for their parallel implementation, they represent a favorable alternative to the modified "parallel" BCJR algorithm proposed in the literature for the application to magnetic channels.     

CO-OFDM系统中利用阈值功率点改进的后向传输补偿算法

采用后向传输(BP)补偿算法补偿相干光正交频分复用(CO-OFDM)通信系统的非线性损伤是一种具有应用前景的方法。本文针对后向传输(BP)补偿算法计算复杂度高的问题,根据相干光正交频分复用(CO-OFDM)系统中非线性损伤与光功率的关系,通过选取合适的阈值功率点降低BP补偿算法的高计算复杂度。仿真结果表明,提出的方法可以大大降低BP补偿算法的计算复杂度,同时克服了传统算法过补偿的缺点,系统性能也得到提升。在100Gbit/s的CO-OFDM系统中,采用衰减因子为0.2dB/km的零色散光纤,信号传输3 200km(32×100km),光信噪比(OSNR)为16dB时,在选取合适的阈值功率点后,所需的BP补偿步骤数目由传统算法N=61降低为N=13,计算复杂度仅为原来的13/61。此外,采用改进的BP补偿算法补偿之后,系统的Q因子增加了0.204dB。     

Gaussian approximation based mixture reduction for near optimum detection in MIMO systems

The optimal "soft" symbol detection for spatial multiplexing multiple input multiple output (MIMO) system with known channel information requires knowledge of the marginal posterior symbol probabilities for each antenna. The calculation of these quantities requires the evaluation of the likelihood function of the system for all possible symbol combinations, which is prohibitive for large systems. It is however most often the case that most of the transmitted symbol combinations contribute only very little to these marginal posterior probabilities. We propose in this paper a suboptimal procedure which identifies the most significant symbol combinations via a sequential algorithm with Gaussian Approximation (SGA). Simulation results show that our method can approach the optimal a posteriori probability detector (APP) performance while being less complex than comparable suboptimal algorithms, such as the sphere decoder (SD). We further demonstrate that as opposed to the SD the complexity and memory requirements of our algorithm are fixed, therefore easing practical implementation.