Two efficient and stable MMSE detection algorithms for the V‐BLAST system

For practical consideration, efficient and stable implementation of the promising vertical Bell Labs Layered Space–Time system is highly desirable. The original square‐root algorithm (SRA), proposed by Hassibi, for the minimum mean square error detection, is composed of three stages, that is, initialization, ordering, and nulling. Unlike the original SRA whose initialization stage is not completely based on unitary transformations, the three stages of our proposed algorithm #1 are all based on unitary transformations. The average number of multiplications required by our proposed algorithm #1 is about (29∕10)M³, where both the numbers of transmit and receive antennas are equal to M. In the meantime, the average number of multiplications required by the original SRA is (17∕3)M³. In addition to the stable initialization, ordering, and nulling considered by the proposed algorithm #1, our proposed algorithm #2 considers stable detection. Our proposed algorithm #2 is completely based on Givens rotations, which is to be implemented by COordinate Rotation DIgital Computer‐based hardwares. Copyright © 2011 John Wiley & Sons, Ltd.     

MIMO系统中一种自适应的迭代检测译码算法

针对常用的非穷尽列表形式后验概率检测算法直接采用恒定且较大的列表长度,导致列表冗余度大的问题,该文提出了一种自适应长度的列表球形译码算法(Adaptive Size List Sphere Decoding, ASLSD)。在算法中通过更新检测半径和设置停止条件,使检测列表长度可随信噪比和迭代次数自适应变化。而且通过将列表操作与LSD (List Sphere Decoding)检测相结合,避免了符号序列在不同半径下的重复检测。仿真表明,在较小性能损失的前提下,该算法可以大大减小所需检测列表的长度,进而有效降低接收机的复杂度。     

MIMO系统中一种新颖的联合信道估计与检测算法

本文基于EM(Expectation-Maximum)算法,提出了一种简单而有效的联合信道估计与检测接收机结构。接收机中通过采用非穷尽列表形式的后验概率检测算法,避免了传统干扰抵消结构中各天线发送信号间的残余干扰对后验概率计算的影响。并进一步针对常用的非穷尽列表形式后验概率检测算法存在的列表冗余度大的问题,提出了自适应长度的列表球形译码算法(ASLSD,Adaptive Size List Sphere Decoding)。该算法通过更新检测半径和设置停止条件,使检测列表长度可随信噪比和迭代次数自适应变化。而且通过将列表操作与LSD(List Sphere Decoding)算法相结合,避免了符号序列在不同半径下的重复检测和排序操作。仿真表明,在复杂度方面,该算法需搜索的路径数远小于LSD算法。在算法性能方面,以3次迭代10~(-4)误码率为例,该算法与PIC算法相比可以获得近2dB的性能增益,因而具有更优的性能与复杂度的折衷。     

MIMO系统中球形解码算法性能仿真比较

球形解码(Sphere Decoder,SD)算法能以较低的复杂度实现多输入多输出(Multiple Input Multiple Output,MIMO)系统的最优检测,是当前受到普遍关注的MIMO检测算法。对当前球形解码的主要研究成果进行综述,根据搜索策略进行分类,重点分析基于深度优先策略的VB、CL和基于宽度优先策略的K-Best、FSD算法,并且讨论了几种初始半径的选择方法,最后在准静态平坦瑞利衰落环境下对上述算法进行了性能仿真比较。     

An efficient tabu search for cell planning problem in mobile communication

During the past decades, mobile communication is in the vigorous development, where the cell planning problem (CPP) is one of impressive research issues. CPP has been proved to be NP‐Complete, and many works develop intelligent heuristic search strategies to solve it. Among many factors to affect the cell planning, the major one is the signaling cost, where the location management is critical to the cost. In this paper, we focus on how to tackle CPP such that the signaling cost can be minimized. We adopt a meta‐heuristic iterative search algorithm, Tabu Search (TS), to deal with the cell planning issue for the base station and propose novel designs to improve the TS capability, including initialization and neighbor swap strategy. The simulation results reveal that our TS outperforms traditional TS, genetic algorithms, and several previous works in CPP. Copyright © 2014 John Wiley & Sons, Ltd.     

Efficient Complex Sphere Decoding for MC-CDMA Systems

Maximum likelihood (ML) joint detection of multi-carrier code division multiple access (MC-CDMA) systems can be efficiently implemented with a sphere decoding (SD) algorithm. In this paper, we examine the application of complex instead of real SD to detect MC-CDMA, which solves many problems in a more elegant manner and extends SD adaptability to any constellation. We first propose a new complex SD algorithm whose efficiency is based on not requiring an estimate of the initial search radius but selecting the Babai point as the initial sphere radius instead; also, efficient strategies regarding sorting the list of possible lattice points are applied. Indeed, complex SD allows complex matrix operations which are faster than real counterparts in double dimension. Next, a novel lattice representation for the MC-CDMA system is introduced, which allows optimum multiuser detection directly from the received signal. This avoids noise whitening operation, and also despreading and equalization procedures are not required further at the receiver side     

Wavelength Converters Placement in All Optical Networks Using Particle Swarm Optimization

Placement of wavelength converters in an arbitrary mesh network is known to be a NP-complete problem. So far, this problem has been solved by heuristic strategies or by the application of optimization tools such as genetic algorithms. In this paper, we introduce a novel evolutionary algorithm: particle swarm optimization (PSO) to find the optimal solution to the converters placement problem. The major advantage of this algorithm is that does not need to build up a search tree or to create auxiliary graphs in find the optimal solutions. In addition, the computed results show that only a few particles are needed to search the optimal solutions of the placement of wavelength converters problem in an arbitrary network. Experiments have been conducted to demonstrate the effectiveness and efficiency of the proposed evolutionary algorithm. It was found that the efficiency of PSO can even exceed 90% under certain circumstances. In order to further improve the efficiency in obtaining the optimal solutions, four strategic initialization schemes are investigated and compared with the random initializations of PSO particles.     

Routing in intermittent networks using storage domains

In this paper, we present a routing algorithm for a class of networks where a contemporaneous end‐to‐end path may not exist at the time of data transfer due to intermittent links. Several examples of such networks exist in the context of sensor networks, mobile ad hoc networks and delay tolerant networks. The proposed routing algorithms follow a priori routing similar to source routing. Link state changes are assumed to be known ahead of time, for instance, due to planned duty cycling resulting in scheduled connectivity. The basic idea behind the proposed routing algorithms is to modify the breadth first search (BFS) algorithm to take into account link state changes and find the quickest route between source and destination nodes. We introduce the idea of time‐varying storage domains where all nodes connected for a length of time act as a single storage unit by sharing the aggregated storage capacity of the nodes. This will help situations where storage is a limited resource. We evaluate the routing algorithm with and without storage domain in an extensive simulation. The delay performance of the proposed algorithms is conceptually the same as flooding‐based algorithms but without the penalty of multiple copies. More significantly, we show that the Quickest Storage Domain (Quickest SD) algorithm distributes the storage demand across many nodes in the network topology, enabling balanced load and higher network utilization. In fact, we show that for the same level of performance, we can actually cut the storage requirement in half using the Quickest SD algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

Comparing Three Heuristic Search Methods for Functional Partitioning in Hardware–Software Codesign

This paper compares three heuristic search algorithms: genetic algorithm (GA), simulated annealing (SA) and tabu search (TS), for hardware–software partitioning. The algorithms operate on functional blocks for designs represented as directed acyclic graphs, with the objective of minimising processing time under various hardware area constraints. Thecomparison involves a model for calculating processing time based on a non-increasing first-fit algorithm to schedule tasks, given that shared resource conflicts do not occur. The results show that TS is superior to SA and GA in terms of both search time and quality of solutions. In addition, we have implemented an intensification strategy in TS called penalty reward, which can further improve the quality of results.     

Low complexity hybrid layered tabu-likelihood ascent search for large MIMO detection with perfect and estimated channel state information

In this work, we proposed a low-complexity hybrid layered tabu-likelihood ascent search (LTLAS) algorithm for large multiple-input multiple-output (MIMO) system. The conventional layered tabu search (LTS) approach involves many partial reactive tabu searches (RTSs), and each RTS requires an initialization and searching phase. In the proposed algorithm, we restricted the upper limit of the number of RTS operations. Once RTS operations exceed the limit, RTS will be replaced by low-complexity likelihood ascent search (LAS) operations. The block-based detection approach is considered to maintain a higher signal-to-noise ratio (SNR) detection performance. An efficient precomputation technique is derived, which can suppress redundant computations. The simulation results show that the bit error rate (BER) performance of the proposed detection method is close to the conventional LTS method. The complexity analysis shows that the proposed method has significantly lower computational complexity than conventional methods. Also, the proposed method can reduce almost 50% of real operations to achieve a BER of 10⁻³.     

融合多尺度分析和水平集的交互式图分割方法

针对快速水平集算法用于图像分割时,存在水平集初始化和阈值设置的困难,该文提出一种融合金字塔模型、随机游走及水平集(PYR-RW-LS)的新算法。首先将多尺度分析引入随机游走算法,把分割结果作为快速水平集算法的初始化曲线,解决其初始化问题;接着把水平集演化看成对曲线上的点不断进行模式分类的过程,引入贝叶斯分类决策和最小距离分类决策交替工作,产生曲线演化所需的驱动力,同时将两种分类决策的失效条件作为新算法迭代停止的条件,解决了快速水平集算法阈值设置的困难。仿真实验结果表明：PYR-RW-LS算法比只采用模式分类思想的快速水平集算法拥有更高的计算效率,且在抗噪性方面亦优于随机游走算法,同时保留了随机游走算法对弱边缘不敏感的优点,尤其适用于大尺寸,高清晰度的图像处理。     

Fast optimal and suboptimal any-time algorithms for CDMA multiuser detection based on branch and bound

A fast optimal algorithm based on the branch-and-bound (BBD) method is proposed for the joint detection of binary symbols of K users in a synchronous code-division multiple-access channel with Gaussian noise. Relationships between the proposed algorithms (depth-first BBD and fast BBD) and both the decorrelating decision-feedback (DF) detector and sphere-decoding algorithm are clearly drawn. It turns out that decorrelating DF detector corresponds to a "one-pass" depth-first BBD; sphere decoding is, in fact, a type of depth-first BBD, but one that can be improved considerably via tight upper bounds and user ordering, as in the fast BBD. A fast "any-time" suboptimal algorithm is also available by simply picking the "current-best" solution in the BBD method. Theoretical results are given on the computational complexity and the performance of the "current-best" suboptimal solution.     

Radius selection for lattice sphere decoder-based block data transmission systems

Lattice sphere decoder (LSD) searches lattice points in space within a certain radius (d), where the closest point obtained is considered the solution. It is well known in LSD, when the initial radius (d) increases, the complexity increase. Therefore, this paper aims to obtain an initial radius (d) exact expression to reduce the system complexity with reasonable performance. The derived expression shows that initial radius (d) depends on lattice dimension n, signal-to-noise ratio (\(\gamma\)), and noise variance \(\sigma^{2}\). Hence, this paper proposed a new LSD for BDTS based on this initial radius technique. The proposed LSD achieves a good balance between complexity and performance. Other analytical expressions for complexity and performance in relation with (d) are also derived. It has been observed that the convergence between the analytical system performance and complexity with their respective simulation results.     

New adaptive partial distortion search using clustered pixel matching error characteristic.

In order to reduce the computation load, many conventional fast block-matching algorithms have been developed to reduce the set of possible searching points in the search window. All of these algorithms produce some quality degradation of a predicted image. Alternatively, another kind of fast block-matching algorithms which do not introduce any prediction error as compared with the full-search algorithm is to reduce the number of necessary matching evaluations for every searching point in the search window. The partial distortion search (PDS) is a well-known technique of the second kind of algorithms. In the literature, many researches tried to improve both lossy and lossless block-matching algorithms by making use of an assumption that pixels with larger gradient magnitudes have larger matching errors on average. Based on a simple analysis, it is found that, on average, pixel matching errors with similar magnitudes tend to appear in clusters for natural video sequences. By using this clustering characteristic, we propose an adaptive PDS algorithm which significantly improves the computation efficiency of the original PDS. This approach is much better than other algorithms which make use of the pixel gradients. Furthermore, the proposed algorithm is most suitable for motion estimation of both opaque and boundary macroblocks of an arbitrary-shaped object in MPEG-4 coding.     

Extending a Fixed-Complexity Sphere Decoder to Obtain Likelihood Information for Turbo-MIMO Systems

A list extension for a fixed-complexity sphere decoder (FSD) to perform iterative detection and decoding in turbo-multiple input-multiple output (MIMO) systems is proposed in this paper. The algorithm obtains a list of candidates that can be used to calculate likelihood information about the transmitted bits required by the outer decoder. The list FSD (LFSD) overcomes the two main problems of the list sphere decoder (LSD), namely, its variable complexity and the sequential nature of its tree search. It combines a search through a very small subset of the complete transmit constellation and a specific channel matrix ordering to approximate the soft- quality of the list of candidates obtained by the LSD. A simple method is proposed to generate that subset, extending the subset searched by the original FSD. Simulation results show that the LFSD can be used to approach the performance of the LSD while having a lower and fixed complexity, making the algorithm suitable for hardware implementation.      

基于改进人工蜂群算法的认知抗干扰智能决策技术研究

在认知抗干扰系统中,智能决策是其核心,根据干扰环境,对系统的干扰抑制方式、频谱资源分配、调制编码方式和功率调整信息进行最优决策。人工蜂群算法（Artificial Bee Colony,ABC）相较于其他群体智能算法全局寻优速度更快,设置参数少、灵活,易与其他技术结合改进原算法,实用性更广泛,但ABC算法同样有其局限性,如局部搜索能力较弱、后期收敛速度慢等。针对复杂干扰环境下对离散参数的决策,本文设计了一种基于改进人工蜂群算法的认知抗干扰智能决策引擎,分析了引擎模型,根据系统效能设计了目标函数和染色体,阐述了决策实现步骤,优化了决策参数,提出了按基因组搜索的改进算法;通过对系统抗干扰性能的仿真,验证了与未采用智能决策的抗干扰系统相比,采用本文提出的智能决策引擎的认知抗干扰系统在干扰环境中不仅具有强抗干扰性能,而且在保证通信传输可靠性的前提下,具有较低的发射功率和高传输效率,与采用传统人工蜂群算法和遗传算法的决策引擎相比,基于改进人工蜂群算法的决策引擎平均收敛代数更少且最优解概率更高。      

Decoding OvTDM with sphere-decoding algorithm

Overlapped time division multiplexing （OvTDM） is a new type of transmission scheme with high spectrum efficiency and low threshold signal-to-noise ratio （SNR）. In this article, the structure of OvTDM is introduced and the sphere-decoding algorithm of complex domain is proposed for OvTDM. Simulations demonstrate that the proposed algorithm can achieve maximum likelihood （ML） decoding with lower complexity as compared to traditional maximum likelihood sequence demodulation （MLSD） or viterbi algorithm （VA）.     

Estimation and setting starting values in ARMA algorithms

For the given observations set of the ARMA (autoregressive moving average) process, the likelihood function depends, not only on model parameters, but on the starting values of the input and output. Therefore, it is called theconditional likelihood function. Theunconditional likelihood function can be obtained in two ways. The first is to set the starting values to zero, as is often done, and the second is to set them to the properly estimated values. The difference between these two types of likelihood functions is significant when the given data sequence is short, and any of the zeros of the moving average part is close to the boundary of the unit circle.In this paper the direct method of starting value estimation and its application to two off-line ARMA estimation algorithms, the maximum likelihood (ML) algorithm and the iterative inverse filtering (ITIF) algorithm, is proposed. Experimental results prove both increased efficiency and stability of these algorithms.The importance of setting the starting values properly is also significant when the recursive algorithm, with previously estimated parameters, has to be restarted. The advantage of the proposed reinitialization method is shown on the recursive lattice algorithm working in the block mode.     

An Efficient Cuckoo Search Algorithm for System-Level Fault Diagnosis

We propose a new efficient algorithm named Cuckoo search fault diagnosis (CSFD) to solve system-level fault diagnosis problem.KMP algorithm is proposed for initialization based on the K-means partition algorithm;a fitness function is designed according to the equation constraints satisfied by the test model;the binary mapping method is advanced by optimizing existing binary mapping algorithm.Experiments show that KMP algorithm significantly reduces the disparity between the initial solution and the actual solution,and CSFD algorithm improves the efficiency and correctness significantly compared with existing typical swarm intelligence diagnosis algorithm.     

Cellular Channel Assignment: A New Localized and Distributed Strategy

As the use of mobile communications systems grows, the need arises for new and more efficient channel allocation techniques. The total number of available channels on a real-world network is in fact a scarce resource, and many assignment heuristics suffer from a clear lack of flexibility (this is the case of Fixed Channel Allocation), or from high computational and communication complexity (as with channel borrowing techniques). Performance can be improved by representing the system with an objective function whose minimum is associated with a good configuration; the various constraints appear as penalty terms in the function. The problem is thus reduced to the search for a minimum, that is often performed via heuristic algorithms like Hopfield neural networks, simulated annealing or reinforcement learning. These strategies usually require a central process to have global information and decide for all cells. We consider an objective-function formulation of the channel assignment problem that has been previously solved by search heuristics; we prove that the search time for the global minimum of the objective function is O(nlogn), and therefore there is no need for search techniques. Finally we show that the algorithm that arises from this formulation can be modified so that global knowledge and synchronization are no longer required, and we give its distributed version. By simulating a cellular network with mobile hosts on a hexagonal cell pattern with uniform call distribution, we show that our technique actually performs better than the best known algorithms.