MDP-Based Network Selection Scheme by Genetic Algorithm and Simulated Annealing for Vertical-Handover in Heterogeneous Wireless Networks

The hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. Markov processes are widely used in performance modelling of wireless and mobile communication systems. We address the problem of optimal wireless network selection during vertical handover, based on the received information, by embedding the decision problem in a Markov decision process (MDP) with genetic algorithm (GA), we use GA to find a set of optimal decisions that ensures the best trade-off between QoS based on their priority level. Then, we emerge improved genetic algorithm (IGA) with simulated annealing (SA) as leading methods for search and optimization problems in heterogeneous wireless networks. We formulate the vertical handoff decision problem as a MDP, with the objectives of maximizing the expected total reward and minimizing average number of handoffs. A reward function is constructed to assess the QoS during each connection, and the AHP method are applied in an iterative way, by which we can work out a stationary deterministic handoff decision policy. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithm that avoid the slow and massive computations. This paper compares the formulation and results of five recent optimization algorithms: artificial bee colony, GA, differential evolution, particle swarm optimization and hybrid of (GA–SA). Simulation results indicated that choosing the SA rules would minimize the cost function, and also that, the IGA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.     

S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. Monte- Carlo simulations show that, after convergence, the performances reached by all near-optimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.     

基于模拟退火算法的多相序列搜索

具有良好相关特性的多相序列是许多有源传感和通信系统的组成部分。由于该序列的搜索是一个非线性多变量的优化问题,寻找高效的搜索方法至关重要。为了获得相关性好的多相序列,文章提出将具有全局优化能力的模拟退火算法引入到多相序列的搜索问题中。基本原理是为序列搜索建立适当的目标函数,调试出适当的退火和停止规则。通过大量对搜索性能和收敛参数进行的数值实验结果来看,文章显示采用模拟退火算法来设计具有良好相关性的多相序列是可行且有效的,特别是当优化问题的参数数量较大时。     

脉冲噪声环境中自适应阵列对信号的跟踪

以alpha稳定分布作为无线信道的噪声模型,基于分数低阶统计量理论采用子空间和近似最大似然的方法,利用整个时隙内的接收信号恢复出期望用户的发射字符。相对于二阶统计量的方法,该方法有更好的韧性和经济的计算量。     

FIR Photonic Microwave Filter Design Employing Simulated Annealing Algorithm

Photonic microwave filters are important parts of fiber‐optic microwave/millimeter wave processing systems. In this paper, the synthesis problem of fiber‐optic FIR microwave filters is addressed and a novel method for designing photonic microwave filter employing a simulated annealing (SA) algorithm is proposed. The design problem of photonic microwave filter can be reduced to be a multi‐variable function optimization problem, which can be solved by a simulated annealing‐based algorithm. As an illustration of the application of SA to photonic microwave filter design, the synthesis of an optimizedFBG‐based photonic microwave filter is discussed. Numerical results show that the global minimum finding capability of SA makes it be an efficient way to design the photonic microwave filter. Numerical results also demonstrate that the proposed method can be applied to design different filtering systems with different constraints.     

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.     

采用DIRECT算法的外辐射源雷达高效直接定位方法

针对直接定位目标函数为非凸函数,传统凸优化方法难以获得全局最优解,而常用网格遍历搜索方法运算量大的问题,提出采用DIRECT (Dividing rectangles)算法进行高效求解的方法。首先建立外辐射源雷达接收信号模型,在此基础上推导最大似然直接定位目标函数,为解决该目标函数非凸难以快速获得全局最优解的问题,将DIRECT算法用于目标位置的快速估计, 并理论分析其计算复杂度。数值仿真表明新方法计算速度快且定位精度高,相比网格遍历法,计算时间降低2个数量级,相比遗传算法降低1个数量级。      

Multiuser Detection Based on Adaptive LMS and Modified Genetic Algorithm in DS-CDMA Communication Systems

In this paper, we present an efficient evolutionary algorithm for the multi-user detection (MUD) problem in direct sequence-code division multiple access (DS-CDMA) communication systems. The optimum detector for MUD is the maximum likelihood (ML) detector, but its complexity is very high and involves an exhaustive search to reach the best fitness of transmitted and received data. Thus, there has been considerable interest in suboptimal multiuser detectors with less complexity and reasonable performance. The proposed algorithm is a combination of adaptive LMS Algorithm and modified genetic algorithm (GA). Indeed the LMS algorithm provides a good initial response for GA, and GA will be applied for this response to reach the best answer. The proposed GA reduces the dimension of the search space and provides a suitable framework for future extension to other optimization algorithms. Our algorithm is compared to ML detector, Matched Filter (MF) detector, conventional detector with GA; and Adaptive LMS detector which have been used for MUD in DS-CDMA. Simulation results show that the performance of this algorithm is close to the optimal detector with very low complexity, and it works better in comparison to other algorithms.     

Hierarchical 3-D Floorplanning Algorithm for Wirelength Optimization

Three-dimensional (3-D) IC physical design problems are usually of higher complexity, with a greatly enlarged solution space due to multiple device structure. In this paper, a new 3-D floorplanning algorithm is proposed for wirelength optimization. Our main contributions and results can be summarized as follows. First, a new hierarchical flow of 3-D floorplanning with a new inter-layer partitioning method. The blocks are partitioned into different layers before floorplanning. A simulated annealing (SA) engine is used to partition blocks with the objective of minimizing the statistical wirelength estimation results. The solution quality is not degraded by the hierarchical flow. Second, floorplans of all the layers are generated in a SA process. Original 3-D floorplanning problem is transformed into solving several 2-D floorplanning problems simultaneously. The solution space is scaled down to maintain a low design complexity. Finally, Experimental results show that our algorithm improves wirelength by 14%-51% compared with previous 3-D floorplanning algorithms. The hierarchical approach is proven to be very efficient and offers a potential way for high-performance 3-D design     

一种基于混合智能算法的协作中继选择新方法

针对多用户多中继场景下协作通信系统的中继选择问题,提出了一种基于混合智能算法的协作中继选择新方法。不同于现有的为每个源节点分配一个中继节点的中继选择方法,新方法建立了为每个源节点分配一个或多个中继节点的优化模型,以最大化多用户多中继协作系统的最小接收信噪比为优化目标,采用结合了模拟退火与遗传算法的混合智能算法来搜寻中继选择问题的最优解。仿真结果表明,所提方法可显著提高目的端的接收信噪比,且算法具有较强的全局搜索和快速寻优能力。     

多数据库集成环境下查询优化研究

在多数据库集成系统中，用户查询由中介系统转换为对各局部数据库的内部操作及局部数据库之间的数据传递。搜索一条访问相互连接数据源的路径以获得综合开销最小的查询策略，是多数据库集成环境向查询优化提出的新的挑战。文章设计了一个新的中介系统，将查询优化问题分为两个阶段，并在此基础上通过化简策略空间、有效的代价估算和策略搜索算法，以提高多数据库环境下查询优化器的工作效率。     

求解约束优化问题的混合粒子群算法

针对约束优化问题提出一种混合粒子群求解算法,该算法根据可行性规则,引入自适应惩罚函数,结合模拟退火算法,不断地寻找更优可行解,逐渐达到搜索全局最优解.通过对一些标准函数测试,计算机仿真结果表明,该方法是有效和可行的,且具有较高的计算精度,相比传统算法,最优解精度达到10-15.     

两种JT和SA下行波束成形联合优化方案

研究了两种基于不同准则下的JT和智能天线下行波束成形联合优化方案,首先给出了采用智能天线的TD-SCDMA下行链路模型,在此基础上给出了两种不同准则下的联合优化算法,然后推导出了权向量,最后就TD-SCDMA系统应用环境进行了仿真和分析,并对两种方案进行了比较分析。这两种方案不仅可以减少智能天线主波束内存在的多用户干扰,而且可以降低移动台的复杂度。     

Linear Pre-coding in MIMO–CDMA Relay Networks

Our aim in this paper is to define a novel beamforming approach in wireless multiple-input-multiple-output (MIMO) code-division-multiple-access (CDMA) relay networks, which involves communication between multiple source-destination pairs. It is assumed that full channel state information of source-relay and relay-destination channels are available. Our design consists of a two-step amplify-and-forward protocol. The first step includes signal transmission from the sources to the MIMO relay, and the second step contains transmitting a version of the linear precoded signal to the destinations. Beamforming is investigated only in MIMO-relay node to reduce end user’s hardware complexity and save the computational power. Accordingly, the optimization problem is defined to find the MIMO relay beamforming coefficients that minimize total relay transmit power by keeping the signal-to-interference-plus-noise ratio (SINR) of all destinations above a certain threshold value. It is shown that such optimization problem is a non-convex quadratically constrained quadratic programming, which is NP-hard in general. However, by relaxing this problem to a semi-definite programming problem, the problem can be solved efficiently. Simulation results verify the performance gain implied by MIMO–CDMA relay system compared to the non-CDMA coded system.     

Optimal radio channel assignment through a new binary dynamic simulated annealing algorithm

The radio channel assignment problem (CAP) is classified as an NP-complete binary optimization problem, which creates the need for faster, yet optimal optimization algorithms to reduce the time of computation when solving such a complex problem. Simulated annealing (SA), a powerful optimal combinatorial search algorithm, was found to be very suitable for CAP. This paper extends the standard capabilities of SA and proposes a new CAP-oriented, quicker binary SA, the binary dynamic SA (BDSA) algorithm, as part of a newly proposed radio channel assignment approach. Simulation results proved that the proposed BDSA has very fast convergence as a stand-alone algorithm and even faster convergence with the newly proposed radio channel assignment approach. © 1998 John Wiley & Sons, Ltd.     

Optimization of Cognitive Radio System Using Simulated Annealing

Cognitive radio (CR) technology has introduced a revolution in wireless communication network and it is capable to operate in a continuously varying radio frequency environment that depends on multiple parameters. In this paper, optimization of CR system has been achieved using simulated annealing (SA) Technique. SA is a stochastic global optimization technique that exploits an analogy between the way in which a metal cools and freezes into a minimum energy crystalline structure. SA has been used to meet the quality of service (QoS) that is defined by the user in terms of minimum transmit power, minimum bit error rate, maximum throughput, minimum interference and maximum spectral efficiency. The results obtained by SA are compared with the genetic algorithm (GA) results for the various QoS parameters and it has been observed that SA is outperforming GA in CR system optimization.     

Simulated Annealing Mechanic Based Noncoherent Signal Detection for Ultra-wideband Sensor Networks

Ultra-wideband (UWB) sensors have extensive commercial and military applications. Unfortunately, coherent signal detection in the presence of intensive multipath propagations may generally become impractical due to complicated realization algorithms and hardware requirements. In this article, we deal with noncoherent UWB signal detection within a promising biological framework, which can also be generalized to the binary-hypothesis target-detection problem, i.e. identification of the presence or absence of a target. Through the developed characteristic representations, signal detection is firstly formulated as a two-group pattern (or target) classification problem in a 2-D feature plane, in which an optimal decision bound can be numerically derived given the supervised training instances. This optimization problem is addressed by using the nature-inspired simulated annealing algorithm (SA), which essentially emulates the physical annealing process of forming the freeze state with the minimum energy. In sharp contrast to traditional optimization techniques, by probabilistically permitting search movement towards worse solutions, SA algorithm can converge to the global optimal with an asymptotical probability of 1. The numerically derived detection performance demonstrated that our present technique is much superior to the existing noncoherent schemes, which provides the appealing signal/target detection architecture for the emerging UWB sensor networks.     

SLM降低峰均功率比MC-CDMA系统的研究

多载波码分多址（MC-CDMA）被认为是移动通信领域最具前景的应用之一,但MC-CDMA信号的高峰均功率比（PAPR）会引起非线性失真并导致误码率性能恶化。选择性映射（SLM）算法是解决该问题的有效方法,但其计算复杂度较高。针对该问题,将SLM算法转化为全局优化问题,采用模拟退火算法（Simulated Annealing.SA）优良的迭代寻优能力,从而用较小的复杂度获得了具有较低峰均功率比。仿真结果验证其有效性。     

一种基于规则的软件体系结构层性能演化优化方法

目前基于规则的软件体系结构（Software Architecture,简记为SA）层性能优化方法大多未充分考虑优化过程中规则的使用次数和使用顺序的不确定性,导致了搜索空间受限而难以获取更优的性能改进方案.针对这一问题并以最小化系统响应时间为优化目标,文中首先定义一种基于规则的SA层性能优化模型RPOM,以将SA层性能优化抽象为求解最优规则序列的数学问题;然后设计一种支持SA层性能改进规则序列执行的框架RSEF;进一步提出一种采用约束检查、修复及统计学习机制的演化求解算法EA4PO;最后以Web应用为案例与已有方法进行实验对比.结果表明：（1）本文方法较已有方法可以获取更短的系统响应时间;（2） EA4PO所引入的统计学习机制可显著提高演化求解算法的收敛速度和解质量.     

Image reconstruction using interval simulated annealing in electrical impedance tomography

Electrical impedance tomography (EIT) is an imaging technique that attempts to reconstruct the impedance distribution inside an object from the impedance between electrodes placed on the object surface. The EIT reconstruction problem can be approached as a nonlinear nonconvex optimization problem in which one tries to maximize the matching between a simulated impedance problem and the observed data. This nonlinear optimization problem is often ill-posed, and not very suited to methods that evaluate derivatives of the objective function. It may be approached by simulated annealing (SA), but at a large computational cost due to the expensive evaluation process of the objective function, which involves a full simulation of the impedance problem at each iteration. A variation of SA is proposed in which the objective function is evaluated only partially, while ensuring boundaries on the behavior of the modified algorithm.