低密度校验码串并行混合迭代译码算法

本文在分析基于校验节点和变量节点的串行置信度传播迭代译码算法的基础上，提出了一种串并行混合置信度传播迭代译码算法。该算法比现有串行算法效率更高，误码率性能十分逼近高复杂度的1阶置信度传播与迭代统计排序结合的并行译码算法。我们的算法是线性复杂度的，可以应用于长码。文中提供了仿真结果，通过对不同译码算法的比较，证实了我们的算法的优秀性能。     

New SNR Adaptive Sphere Decoding Algorithm

针对多输入多输出(MIMO)无线通信系统中基于球形译码算法(Sphere Decoding Algorithm,SDA)在低信噪比区域较高的复杂度,提出一种半定松弛算法和有限星座SDA相结合的信噪比自适应的SDA.通过仿真得知,所提出的算法与已有的SDA相比,在低信噪比区域有较低的算法复杂度,并且误比特性能逼近于最优的SDA.     

一种新的信噪比自适应的球形译码算法

针对多输入多输出(MIMO)无线通信系统中基于球形译码算法(Sphere Decoding Algorithm,SDA)在低信噪比区域较高的复杂度,提出一种半定松弛算法和有限星座SDA相结合的信噪比自适应的SDA。通过仿真得知,所提出的算法与已有的SDA相比,在低信噪比区域有较低的算法复杂度,并且误比特性能逼近于最优的SDA。     

基于贪心策略的大规模MIMO系统信号检测算法

针对传统球形译码性能和计算复杂度受到初始半径及搜索策略制约的问题,提出了一种新的基于M算法的贪心策略球形译码检测算法,对树搜索的方法进行了改进,先将该层信号集合中的距离增量进行排序,然后选择距离增量最小的M个点为信号点,这样每一次选取的信号点相对该层都是局部最优的.仿真结果表明,相比于传统球形译码检测算法,当M为1时,该算法可以降低约30％的计算复杂度.使球形译码算法的效率得到了很大的提高,可以运用于大规模MIMO系统中.     

重排序K-Best球形译码算法

为了提高无线通信系统的频谱利用率,降低最大似然接收机的复杂度。文中在基于球形译码的MIMO接收机并在保证接近最大似然接收机性能的前提下,给出了在K-Best球形译码算法的基础上对K-Best球形译码算法进行重排序的实现方法。     

一种MIMO系统中改进型球形译码算法

在总结MIMO系统各种信号检测算法的基础上,提出一种基于ML并结合排序QR分解的改进型球形译码算法。对最先检测的d层星座点进行遍历,提高最先检测级的性能,剩余层的星座点采用SD算法检测。仿真表明,所提出改进算法能获得较好的ML检测性能,且复杂度相对较低。     

降低高条件数信道下的球形译码算法复杂度的方法

MIMO系统中,球形译码可以在保证接近ML检测性能的前提下大大降低检测复杂度。但当信道矩阵条件数很高时,球形译码的复杂度仍然会很高。在分析了这一现象的原因后,本文提出了在高层对权值进行合并的一种球形译码算法,因为其减小了译码搜索过程中对树的高层节点的访问的概率,由此降低了搜索复杂度。仿真结果表明,这种算法在低信噪比、高条件数时可以节约20％的浮点运算操作次数。     

一种改进的无线光通信LDPC码译码算法

针对无线光通信中低密度奇偶校验码(LDPC)置信传播(BP)译码算法复杂度高及置信度振荡造成译码错误等缺点,基于对数BP算法提出了一种改进的译码算法。改进的译码算法在校验节点运算时,判断输入到校验节点消息的最小值与某个门限的大小,根据比较结果,分别用消息最小值或若干个最小值进行运算,在损失很少性能的情况下降低了运算复杂度;同时在比特节点采用振荡抵消处理运算,提高了算法的性能增益。最后在对数正态分布湍流信道模型下,分别对比特充分交织和交织深度为16的情况进行了仿真实验。仿真结果表明,改进的译码算法与BP算法相比,大幅度降低了计算复杂度,而且译码性能有一定的优势,收敛速度损失很少;而相对于最小和算法,改进的算法虽然译码复杂度有所增加,但误码率性能有明显的优势,并且收敛速度也优于最小和算法。因此,改进的译码算法是无线光通信中LDPC码译码算法复杂度和性能之间一个较好的折中处理方案。     

多元LDPC码列分层DBC译码算法

针对多元LDPC码扩展最小和（Extended Min Sum,EMS）译码算法收敛速度慢、运算复杂度高的问题,提出一种多元LDPC码列分层动态检泡（Dynamic Bubble-Check,DBC）译码算法。首先对变量节点按不同列重进行分层处理,译码时率先更新列重较大分层的变量节点消息,不同层之间采用串行方式进行消息传递,通过并串结合的方式降低译码迭代次数。在校验节点消息更新过程中,采用动态检泡方法减少EMS算法中的运算量,降低算法复杂度。仿真结果表明,在几乎不损失性能的前提下,该算法的平均最大迭代次数仅为EMS译码算法的50%,复杂度降低为EMS算法的50%。     

基于置信度震荡的LDPC码译码算法研究

在采用基于对数似然比的置信度传递译码算法LLR-BP(Log-Likelihood Ratio Belief Propagation)对低密度奇偶校验码LDPC code(Low-density Parity-check code)译码时,变量节点对数似然比外信息ex-LLR(Extrinsic Log-Likelihood Ratio)的震荡会引起比特节点发生错误.文中分析了ex-LLR值和后验概率值(APP)产生震荡的原因及两者的关系,提出了减弱其对整个译码过程影响的两种改进译码算法.仿真结果表明,改进的译码算法在基本不增加译码复杂度的情况下,能够降低译码错误平层,进一步改善传统的LLR-BP译码算法的性能.     

Reduced complexity closest point decoding algorithms for random lattices

Abstract-Closest point algorithms find wide applications in decoding block transmissions encountered with single- or multiuser communication links relying on a single or multiple antennas. Capitalizing on the random channel and noise models typically encountered in wireless communications, the sphere decoding algorithm (SDA) and related complexity-reducing techniques are approached in this paper from a probabilistic perspective. With both theoretical analysis and simulations, combining SDA with detection ordering is justified. A novel probabilistic search algorithm examining potential candidates in a descending probability order is derived and analyzed. Based on probabilistic search and an error-performance-oriented fast stopping criterion, a computationally efficient layered search is developed. Having comparable decoding complexity to the ing-canceling (NQ algorithm with detection ordering, simulations confirm that the novel layered search achieves considerable error-performance enhancement.     

用于病态MIMO系统的一种快速球形译码算法

如今的球形译码算法研究,多是针对MIMO系统的传输矩阵满秩的情况。然而现实常常出现传输矩阵是奇异阵的情况即传输矩阵为病态矩阵,原先的球形译码算法将不再适用。本文提出了一种快速的球形译码算法,设法将病态矩阵变为满秩矩阵,并利用迫零检测估计来对最初的半径取值加以限制,大大加快了搜索速度,而性损失却很小。     

DOA estimation in the time–space CDMA system using modified particle swarm optimization

In this paper, we consider the problem of estimating the direction-of-arrival (DOA) of code-division multiple access (CDMA) signals. It has been shown that the searching complexity and estimating accuracy of the conventional spectral searching methods strictly depend on the number of search grids used during the search. It is time consuming and the required number of search grids is not easy to determine. For the purpose of efficient estimation, a highly efficient approach has been proposed that is implemented on polynomial rooting rather than spectral searching. However, this rooting method is suboptimal in the presence of the noise and multiple access interference (MAI). As proposed in this paper, a novelty particle swarm optimization (PSO) with the proposed techniques offers a much faster convergence compared to the PSO with hard-constraints. In conjunction with a modified PSO for angle searching, the proposed approach can achieve the advantages of reducing search complexity and more accurate estimate over existing conventional spectral searching method. Finally, several computer simulation examples are provided for illustration and comparison.     

Computationally efficient DOA estimation for coprime linear array: a successive signal subspace fitting algorithm

ABSTRACT

In this paper, direction of arrival (DOA) estimation of multiple signals with coprime array is investigated and signal subspace fitting (SSF) method is linked to the coprime array, which achieves a better DOA estimation performance than the traditional uniform array. While the SSF method requires expensive computational cost in the case of multiple signals due to the multidimensional global angular searching, we propose a successive SSF (S-SSF) algorithm from a computationally efficient perspective. In the proposed algorithm, we employ rotational invariance and coprime property to obtain the initial estimates. Then, via a successive scheme, we transform the traditional multidimensional global angular searching problem into one-dimensional partial angular searching one. Consequently, the computational complexity has been significantly reduced. Specifically, the proposed S-SSF algorithm can obtain almost the same DOA estimation performance as SSF but with remarkably lower complexity. Finally, Cramer-Rao Bound (CRB) is provided and numerical simulations demonstrate the effectiveness of the proposed algorithm.     

Reducing computational complexity using transmitter correlation value‐assisted schedulers in multiuser MIMO uplink

In this paper, a threshold bounded antenna selection scheduler (TBS) and a computational complexity bounded antenna selection scheduler (CCBS) are proposed to reduce computational complexity in multiple input multiple output (MIMO) uplink. In contrast to previous works, a spatially correlated MIMO channel model is considered and a transmitter correlation value (TCV) is newly introduced to assist the antenna selection in addition to the channel gain. For the TBS or CCBS, with predetermined threshold of TCV or ratio of successful antennas (RSAs), full searching (FS) and sub searching (SS) are applied more efficiently to user equipments (UEs) compared with previous schedulers. As a result, the number of candidate antennas in the scheduling set can be reduced, which translates into a lower computational complexity in terms of number of evaluated antenna combinations. Additionally, compared with the TBS, the peak computational complexity can be further reduced by the CCBS. Simulation results show that with proposed schedulers the computational complexity can be reduced by at least 50% with an acceptable compromise of capacity. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimizing Electrode Placement Using Finite-Element Models in Radiofrequency Ablation Treatment Planning

Conventional radiofrequency ablation (RFA) planning methods for identifying suitable electrode placements typically use geometric shapes to model ablation outcomes. A method is presented for searching electrode placements that couples finite-element models (FEMs) of RFA together with a novel optimization strategy. The method was designed to reduce the need for model solutions per local search step. The optimization strategy was tested against scenarios requiring single and multiple ablations. In particular, for a scenario requiring multiple ablations, a domain decomposition strategy was described to minimize the complexity of simultaneously searching multiple electrode placements. The effects of nearby vasculature on optimal electrode placement were also studied. Compared with geometric planning approaches, FEMs could potentially deliver electrode placement plans that provide more physically meaningful predictions of therapeutic outcomes.     

Sphere decoding algorithms with improved radius search

We start by identifying a relatively efficient version of sphere decoding algorithm (SDA) that performs exact maximum-likelihood (ML) decoding. We develop novel algorithms based on an improved increasing radius search (IIRS), which offer error performance and decoding complexity between two extremes: the ML receiver and the ing-canceling (NC) receiver with detection ordering. With appropriate choices of parameters, our IIRS offers the flexibility to trade error performance for complexity. We provide design intuitions and guidelines, analytical parameter specifications, and a semianalytical error-performance analysis. Simulations illustrate that IIRS achieves considerable complexity reduction, while maintaining performance close to ML.     

Fast algorithm based on the sole- and multi-depth texture measurements for HEVC intra coding

In High Efficiency Video Coding (HEVC), intra coding plays an important role, but also involves huge computational complexity due to a flexible coding unit (CU) structure and a large number of prediction modes. This paper presents a fast algorithm based on the sole- and multi-depth texture measurements to reduce the complexity from CU size and prediction mode decisions. For the CU size decision, evaluation results in the CU coding with one and multiple depths are utilized to classify CUs into heterogeneous, homogeneous, depth-prominent and other ones. Fast CU size decisions are made for different kinds of CUs. For the prediction mode decision, the tendencies for different CU sizes are detected based on multiple depths. The number of searching modes is decreased adaptively for the CU size with fewer tendencies. Experimental results show the proposed algorithm by off-line training reduces 53.32% computational complexity, with 1.47% bit-rate increasing.     

PAPR Reduction of OFDM Signals Using Partial Transmit Sequences With Low Computational Complexity

Partial transmit sequences (PTS) is one of the attractive techniques to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) system. As conventional PTS technique requires an exhaustive searching over all the combinations of the given phase factors, which results in the computational complexity increases exponentially with the number of the sub-blocks. In this paper, we aim to obtain the desirable PAPR reduction with the low computational complexity. Since the process of searching the optimal phase factors can be categorized as combinatorial optimization with some variables and constraints, we propose a novel scheme, which is based on a nonlinear optimization approach named as simulated annealing (SA), to search the optimal combination of phase factors with low complexity. To validate the analytical results, extensive simulations have been conducted, showing that the proposed schemes can achieve significant reduction in computational complexity while keeping good PAPR reduction.