Fast codevector search scheme for 3D mesh model vector quantisation

A new fast approach to the nearest codevector search for 3D mesh compression using an orthonormal transformed codebook is proposed. The algorithm uses the coefficients of an input vector along a set of orthonormal bases as the criteria to reject impossible codevectors. Compared to the full search algorithm, a great deal of computational time is saved without extra distortion and additional storage requirement. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of computational complexity.     

Filtering and Searching Vector Quantization

Under the consideration of computational complexity and design regularity, in this paper, a FASVQ (filtering and searching vector quantization) is presented to compress images. FASVQ utilizes a heuristic to filter codevectors with small costs and then employs full-search VQ within the surviving codevectors. We have proven that the proposed heuristic can easily be implemented by a table lookup technique and over 95% codevectors can be filtered. Although, the quantized codevector of FASVQ wouldn't be optimal, the experimental results show that the PSNR degradation between full-search VQ and FASVQ is only 0.24 dB on the average.     

Pseudo-Gray coding

A pseudo-Gray code is an assignment of n-bit binary indexes to 2" points in a Euclidean space so that the Hamming distance between two points corresponds closely to the Euclidean distance. Pseudo-Gray coding provides a redundancy-free error protection scheme for vector quantization (VQ) of analog signals when the binary indexes are used as channel symbols on a discrete memoryless channel and the points are signal codevectors. Binary indexes are assigned to codevectors in a way that reduces the average quantization distortion introduced in the reproduced source vectors when a transmitted index is corrupted by channel noise. A globally optimal solution to this problem is generally intractable due to an inherently large computational complexity. A locally optimal solution, the binary switching algorithm, is introduced, based on the objective of minimizing a useful upper bound on the average system distortion. The algorithm yields a significant reduction in average distortion, and converges in reasonable running times. The sue of pseudo-Gray coding is motivated by the increasing need for low-bit-rate VQ-based encoding systems that operate on noisy channels, such as in mobile radio speech communications     

Low complexity signal detection algorithm for MIMO-OFDM systems

A very low complexity algorithm for ordering based on the MMSE criterion and detecting based on the QR decomposition of sorted channel matrix in BLAST architectures is presented. The algorithm needs only a fraction of computational effort compared to the standard ordering MMSE algorithm and achieves suboptimal performance.     

粒子对算法在图像矢量量化中的应用

本文给出了一种新的图像矢量量化码书的优化设计方法——粒子对算法.在传统粒子群优化(Particle Swarm Optimization,PSO)算法的基础上,用两个粒子构成了群体规模较小的粒子对,在码书空间中搜索最佳码书.在每次迭代运算中,粒子对按先后顺序执行PSO算法中的速度更新、位置更新操作和标准LBG算法,并用误差较大的训练矢量代替越界的码字.此算法避免粒子陷入局部最优码书,较准确地记录和估计每个码字的最佳移动方向和历史路径,在训练矢量密集区域和稀疏区域合理地分配码字,从而使整体码书向全局最优解靠近.实验结果表明,本算法始终稳定地取得显著优于FKM、FRLVQ、FRLVQ-FVQ算法的性能,较好地解决了矢量量化中初始码书影响优化结果的问题,且在计算时间和收敛速度方面有相当的优势.     

Low-complexity systolic V-BLAST architecture

In multiple-input multiple-output systems, an ordered successive interference canceller, termed the vertical Bell laboratories layered space-time (V-BLAST) algorithm, offers good performance. This letter presents a low-complexity VBLAST scheme suited for parallel implementation. The proposed scheme, using a greedy ordering, can achieve a performance comparable to that of V-BLAST with optimum ordering, while its computational complexity is lower than a linear detector.     

等误差原则在进化算法优化矢量量化中的应用

文中利用进化算法优化矢量量化器设计,在选择后代码书矢量时,利用等误差原则选择获胜后代码书矢量.算法采用LBG算法作为基本聚类算法,利用所选后代码矢调整相应区域的父代码矢,减小各区域子误差,改善总的期望误差.试验证明了此方法的有效性,解决了LBG算法局部最优的局限,获取更接近全局最优的码书.     

采用相位判决的低复杂度广义空间调制检测算法

针对广义空间调制(GSM)系统中信号检测复杂度过高的问题,提出了一种基于相位判决的低复杂度检测算法.首先根据一种排序准则对天线组合进行排序,然后将排序后的天线组合中的符号向量依次通过基于相位判决的迫零(ZF)均衡器进行检测,最终得到星座调制符号和激活天线组合.分析和仿真结果表明,该检测算法可以有效缩小接收端的搜索范围,在提供与最大似然(ML)检测算法相近的误比特率(BER)性能的同时,计算复杂度降低了98％.     

多用户多天线系统中基于THP的低复杂度调度排序算法

本文提出了下行多用户多天线系统中一种新的基于Tomlinson-Harashima预编码(THP)的低复杂度调度排序算法.该算法根据THP与误符号率的特点,将调度和排序结合在一起,有效降低了复杂度.仿真结果表明,本文提出的算法在平均误符号率性能上逼近贪婪调度best-first排序,而复杂度更低,在实际系统中有很好的应用价值.     

时频双选信道OFDM系统的ICI消除与均衡

在正交频分复用（OFDM）系统中,因高速移动造成的多普勒效应导致子载波间正交性的破坏并产生载波间干扰（ICI）。为了消除ICI,本文通过分析ICI分布特性及带状矩阵特性,提出了低复杂度的迭代干扰抵消算法和基于最小均方误差准则的排序串行干扰抵消(MMSE-OSIC)算法。仿真结果表明,与传统子载波间干扰频域均衡算法相比,新算法在计算复杂度和性能之间取得了良好的平衡,且MMSE-OSIC算法可以利用时变信道的时间分集特性在高信噪比情况下有效地消除“地板效应”。      

基于网格模型的运动估计技术

网格模型的运动估计和跟踪在基于模型的编码方法中占有重要的地位,在现有的网格运动估计技术中,六边形匹配(Hexagonal Matching)是一种有效的网格模型运动估计算法,它采用局部最优和迭代策略相结合的方法得到较好的全局运动估计结果.六边形匹配算法由块搜索和迭代六边形匹配两步组成,同所有的基于模型的运动估计技术一样,六边形匹配算法也存在运算量大的问题难于满足实时编码需要.本文对六边形匹配算法从三个方面进行改进:用四步搜索替代全搜索、快速的纹理映射技术和有效的节点排序减少迭代次数.改进后的算法在基本上保证原算法的运动估计效率的情况下,算法的复杂度有明显的降低,使得基于模型的编码技术向实用化前进了一步.     

Simplified SCL decoding algorithm of polar codes based on critical sets

In order to reduce the high complexity of the successive cancellation list (SCL) algorithm for polar codes, a simplified SCL decoding algorithm based on critical sets (CS-SCL decoding algorithm) is proposed. The algorithm firstly constructs the critical sets according to the channel characteristics of the polar codes as well as comprehensively considering both the minimum Hamming weight (MHW) of the information bits and the channel reliability. The information bits within the critical sets and the path splitting are still performed by the SCL decoding algorithm while the information bits outside the critical sets are directly performed by the hard decision. Thus, the number of path ordering, copying, and deleting can be reduced during decoding. Furthermore, the computational complexity of the SCL decoding can also be reduced. Simulation results demonstrate that the decoding complexity of the proposed CS-SCL decoding algorithm, compared with the conventional SCL decoding algorithm, is reduced by at least 70%, while compared with the simplified SCL (PS-SS-SCL) algorithm which constructs the critical set with the first and second information bits of the Rate-1 nodes, its decoding complexity can also be reduced. Moreover, the loss of the error correction performance for the proposed CS-SCL decoding algorithm is minor. Therefore, the proposed CS-SCL algorithm is effective and can provide a reasonable tradeoff between the decoding performance and complexity for the decoding algorithm of polar codes.     

一种基于FMO置乱的视频加密算法研究

通过分析H．264提出的灵活宏块排序（FMO）的特点,提出了一种随机置乱FMO—map的视频加密方案。此方案通过产生一个随机顺序的码表来置乱FMO—map来达到视频流加密的效果,另外将加密后的随机置乱码表并行输出。通过分析证明此方案在安全性、压缩比改变率和计算复杂度方面都具有良好的性能,能使视频图像的传输具有较好的安全性和实时性。     

An extension to the ordered subcarrier selection algorithm (OSSA)

In this letter, we propose an extension to the ordered subcarrier selection algorithm (OSSA) for orthogonal frequency division multiplexing (OFDM) systems. The result is a simple algorithm for minimizing the bit error rate of the OFDM system at a fixed throughput. The proposed algorithm employs multiple modulations (non-uniform bit loading) within an OFDM symbol. However, unlike existing bit loading algorithms that have a very high computational complexity, the proposed algorithm is based only on the ordered statistics of the subcarrier gains and is consequently very simple. After ordering the subcarriers based on their gains, progressively higher order modulations are used with increasing gains. The key aspect here that greatly simplifies the algorithm is that the modulation used on a subcarrier depends only on the position of its gain in the ordered set and not on the actual values of the gains. We show an analytical approach for determining the parameters of the algorithm.     

Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

Massive multiple‐input multiple‐output (MIMO) plays a crucial role in realizing the demand for higher data rates and improved quality of service for 5G and beyond communication systems. Reliable detection of transmitted information bits from all the users is one of the challenging tasks for practical implementation of massive‐MIMO systems. The conventional linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) achieve near‐optimal bit error rate (BER) performance. However, ZF and MMSE require large dimensional matrix inversion which induces high computational complexity for symbol detection in such systems. This motivates for devising alternate low‐complexity near‐optimal detection algorithms for uplink massive‐MIMO systems. In this work, we propose an ordered sequential detection algorithm that exploits the concept of reliability feedback for achieving near‐optimal performance in uplink massive‐MIMO systems. In the proposed algorithm, symbol corresponding to each user is detected in an ordered sequence by canceling the interference from all the other users, followed by reliability feedback‐based decision. Incorporation of the sequence ordering and the reliability feedback‐based decision enhances the interference cancellation, which reduces the error propagation in sequential detection, and thus, improves the BER performance. Simulation results show that the proposed algorithm significantly outperforms recently reported massive‐MIMO detection techniques in terms of BER performance. In addition, the computational complexity of the proposed algorithm is substantially lower than that of the existing algorithms for the same BER. This indicates that the proposed algorithm exhibits a desirable trade‐off between the complexity and the performance for massive‐MIMO systems.     

Minimax partial distortion competitive learning for optimalcodebook design

The design of the optimal codebook for a given codebook size and input source is a challenging puzzle that remains to be solved. The key problem in optimal codebook design is how to construct a set of codevectors efficiently to minimize the average distortion. A minimax criterion of minimizing the maximum partial distortion is introduced in this paper. Based on the partial distortion theorem, it is shown that minimizing the maximum partial distortion and minimizing the average distortion will asymptotically have the same optimal solution corresponding to equal and minimal partial distortion. Motivated by the result, we incorporate the alternative minimax criterion into the on-line learning mechanism, and develop a new algorithm called minimax partial distortion competitive learning (MMPDCL) for optimal codebook design. A computation acceleration scheme for the MMPDCL algorithm is implemented using the partial distance search technique, thus significantly increasing its computational efficiency. Extensive experiments have demonstrated that compared with some well-known codebook design algorithms, the MMPDCL algorithm consistently produces the best codebooks with the smallest average distortions. As the codebook size increases, the performance gain becomes more significant using the MMPDCL algorithm. The robustness and computational efficiency of this new algorithm further highlight its advantages.     

Improved layered MIMO detection algorithm with near-optimal performance

A new layered symbol detection algorithm for multiple-input multiple-output systems is presented. In this scheme, the layers are divided into two groups and detected differently. For the layer with the smallest post-detection signal-to-noise ratio, an exhaustive search is performed over the signal constellation; for the remaining layers the conventional iterative vertical Bell laboratories layered space-time (V-BLAST) technique is utilised. The proposed algorithm also uses a different symbol detection ordering from that recommended in the original V-BLAST algorithm. Simulation results show that the error rate performance of the proposed detection algorithm approaches closely that of an optimal maximum likelihood detector with no reduction in the symbol detection throughput, while the computational complexity is less than |Q| times that of V-BLAST, where |Q| is the cardinality of signal constellation Q.     

Efficient Ordering Schemes for High-Throughput MIMO Detectors

The order of the channel matrix columns is an important factor that affects the computational complexity, memory requirement, and detection error rate of multi-input multi-output (MIMO) detectors. Novel efficient ordering schemes proposed in our previous work lead to advantages in the computational complexity and memory requirement of various maximum likelihood (ML) MIMO detectors. In this paper, we apply these ordering schemes to the K-Best detector—a near-ML MIMO detector suitable for high-throughput hardware implementations—and show that our novel ordering schemes improve the performance of the K-Best detector, especially when K is small. With a given detection error rate, our ordering schemes either lead to signal-to-noise ratio (SNR) gains, or allow even smaller K. Two of these ordering schemes, which can be easily embedded into the QR decomposition, are implemented in hardware. We adopt Givens based ordering schemes, due to their numerical stability when fixed point representations are used. Our hardware implementation results show that our novel ordering schemes incur negligible overheads and are particularly suitable for high-throughput implementations.     

Relay ordering in a multi-hop cooperative diversity network

In this paper, we first propose an optimum relay ordering algorithm for the multi-branch multi-hop cooperative diversity networks. This optimum algorithm has a high complexity that makes it hard to implement. Therefore, a suboptimum relay ordering algorithm, which considerably reduces the complexity, is then developed. Furthermore, for a cooperative network with two relays, we analytically evaluate the performance of the suboptimum algorithm by using an approximate end-to-end signal-to-noise ratio expression. Specifically, an approximate probability of wrong selection and an approximate expression of the symbol error rate are derived. The analysis and the numerical results demonstrate that the suboptimum algorithm performs very well as the optimum one at a much lower complexity.     

Fast decoding of the Leech lattice

An efficient algorithm is presented for maximum-likelihood soft-decision decoding of the Leech lattice. The superiority of this decoder with respect to both computational and memory complexities is demonstrated in comparison with previously published decoding methods. Gain factors in the range of 2-10 are achieved. The authors conclude with some more advanced ideas for achieving a further reduction of the algorithm complexity based on a generalization of the Wagner decoding method to two parity constraints. A comparison with the complexity of some trellis-coded modulation schemes is discussed. The decoding algorithm presented seems to achieve a computational complexity comparable to that of the equivalent trellis codes