基于最小二乘支持向量机的衰落信道预测算法

该文探讨了利用相空间重构和支持向量机进行衰落信道非线性预测算法。该算法基于多径衰落信道具有混沌行为,利用坐标延迟理论,重建衰落信道系数的相空间,再根据混沌吸引子的稳定性和分形性,在相空间中通过递归最小二乘支持向量机(RLS-SVM)进行预测。该算法对原始数据可以进行更平滑的处理,在噪声环境下预测的时间范围更长。对时间跨度为63.829ms的衰落系数进行了预测,仿真结果表明,在信噪比为15dB时,预测结果优于AR算法。     

基于最小二乘支持向量机的Jakes衰落信道预测

将LS-SVM用于Jakes衰落信道预测,进而提出了一种新的衰落信道预测算法.该算法利用衰落信道系数的既有观测值构建学习样本,然后借助LS-SVM的学习与判决能力实施非线性预测.对Jakes衰落信道的预测实验表明,文中预测算法可行且有效.另外,在实验中也讨论了嵌入维参数对预测准确度的影响,并给出最优嵌入维的选取方法.     

平坦衰落信道下基于NLS的快速频偏估计算法

文章推导了一种平坦衰落信道下基于非线性最小二乘的频偏估计算法,与已有的其他估计算法不同,该算法无需预知任何信道的相关信息仍具有较好的估计性能.在此基础之上给出了一种简化的非线性最小二乘快速估计算法.仿真结果和运算量分析表明,所给出的简化快速估计算法在获得与完整结构估计算法相同性能的前提下,大大降低了算法的实现复杂度.     

基于混沌交织的Turbo码及其性能仿真

基于非线性混沌映射提出了一种S随机混沌交织算法。在不同遮蔽程度的卫星移动衰落信道模型中,使用不同的解码算法,通过仿真比较了不同伪随机交织算法的Turbo码性能、不同的交织长度对系统性能的影响。仿真结果表明,在信噪比较低的卫星衰落信道中采用MAP、SOVA译码算法时,使用S随机混沌交织器的Turbo码性能均较使用伪随机交织器的Tur-bo码的性能有明显改进。     

混沌扩频通信系统在多径Rayleigh衰落信道中的性能分析

本文研究了混沌直接扩频信号在多径Rayleigh衰落信道中传输时的抗多径干扰和多址干扰的特性。对于所给出的混沌扩频通信系统,当加入多径干扰和信道噪声时,给出了理论误码率与实际误码率的比较,数值结果表明在多径衰落信道中,混沌扩频通信系统抗多径干扰的性能很好;同时对混沌直接扩频信号的奇、偶互相关函数进行了计算,结果表明混沌扩频通信系统具有良好的抗多址干扰能力。     

混沌模拟信息转换——基于多射法的稀疏信号重构

混沌压缩采样是应用混沌系统实现非线性测量的压缩采样理论。该文研究模拟信号的混沌压缩采样-混沌模拟信息转换。该转换通过稀疏信号激励混沌系统,低速采样系统输出实现;信号重构则以混沌系统参数估计理论为基础,通过稀疏正则化的非线性最小二乘问题进行求解。该文将多射法(MS)与迭代再加权非线性最小二乘算法(IRNLS)结合,给出混沌模拟信息转换的MS-IRNLS信号重构算法。文中以Lorenz系统为例,仿真验证了MS-IRNLS算法的重构性能,结果表明方法的有效性。     

一种抗飞行器衰落的快速均衡算法

对流层散射通信信道为时变多径信道,当飞行器飞越散射通信链路会导致飞行器衰落。针对飞行器衰落,提出了一种收敛速度快、跟踪能力强、数值稳定性高、复杂度低的快速自适应均衡算法——基于选择更新的累积误差递归最小二乘自适应均衡算法。根据指数加权最小二乘准则,推导出累积误差递归最小二乘算法,依据共轭斜量算法提出抽头系数选择更新准则。均衡算法的复杂度分析和仿真实验表明提出的快速自适应均衡算法不仅复杂度低,而且有效地提高了均衡器克服信道时间衰落的能力。     

Turbo CPM系统Log-MAP译码算法优化

探讨基于Turbo原理进行遮代检测的编码连续相位调制(CPM)系统在无限瑞利衰落信道下的Log-MAP泽码算法.根据CPM的分解模型,CPM分解为连续相位编码(CPE)与无记忆调制(MM)的组合.基于CPE的记忆特性和递归特性,结合外部的卷积码及交织器,建立串行级联Turbo CPM系统模型.提出衰落信道下基于加权外信息交换的Turbo CPM改进译码算法,有效改善系统收敛性.并探讨不同系统参数对系统性能的影响.     

新的MIMO-OFDM 系统盲频偏估计算法

针对瑞利衰落信道,提出了一种新的基于加权循环前缀(CP,cyclic prefix)的MIMO-OFDM系统频偏估计算法及其简化实用算法。根据最大比合并原理,降低了MIMO-OFDM系统符号间干扰和高斯白噪声对频偏估计性能的影响。然后,利用无线信道统计信息,得到其简化算法。仿真结果表明,此算法在瑞利衰落信道中可以取得良好的频偏估计性能并优于其他同类算法。     

基于马尔可夫过程的卫星移动信道模型及长期预测方法

卫星移动信道可被描述为基于有限状态马尔可夫过程的衰落模型,该文分析了卫星信道的可预测性,然后基于加权预测思想提出了一种卫星移动信道长期预测方法,该方法在当前信道采样的基础上进行二次采样,采样频率大于马尔可夫状态转移速率的2倍,利用信道状态的相关性和信道状态转移概率信息来加权预测未来长期内的信道状态,并依据自回归预测模型给出信道预测输出值,仿真结果表明,采用此方法对卫星信道未来的信道状态进行预测,在信噪比较高时均方误差能够达到10-2量级,在自适应传输过程中可以降低系统平均误比特率,且能够提高系统吞吐量性能,这对卫星移动通信系统的自适应传输和自适应资源分配都具有一定的指导意义。     

Noise reduction for fast fading channel by Recurrent Least Squares Support Vector Machines in embedding phase spaces

A new strategy for noise reduction of fast fading channel is presented. Firstly, more information is acquired utilizing the reconstructed embedding phase space. Then, based on the Recurrent Least Squares Support Vector Machines （RLS-SVM）, noise reduction of the fast fading channel is realized. This filtering technique does not make use of the spectral contents of the signal. Based on the stability and the fractal of the chaotic attractor, the RLS-SVM algorithm is a better candidate for the nonlinear time series noise-reduction. The simulation results shows that better noise-reduction performance is acquired when the signal to noise ratio is 12dB.     

基于量子混沌粒子群优化算法的分数阶超混沌系统参数估计

为了对分数阶超混沌系统中的未知参数进行准确估计,提出一种量子混沌粒子群优化算法（Quantum chaos particle swarm optimization,QCPSO）.该算法通过对量子粒子群优化算法（Quantum behaved particle swarm optimization,QPSO）的实现机理进行分析,并结合量子纠缠与混沌系统之间的相关性而实现.首先,将量子势阱中心视为混沌吸引子围绕的不动点,处于吸引子外部的粒子会逐渐聚集于吸引子之内,而处于吸引子内部的粒子会出现快速分离扩散的现象;然后,采用基于随机映射的粒子更新机制,充分保证混沌粒子的初值多样性;最后,提出了基于不动点中心的尺度自适应策略,解决了算法后期的搜索停滞问题.运用QCPSO算法对典型分数阶超混沌系统参数进行估计,结果表明,该算法在收敛速度与精度上优于改进的差分进化算法、自适应人工蜂群算法以及改进的量子粒子群优化算法.     

On the Performance of FFT/DWT/DCT-based OFDM Systems with Chaotic Interleaving and Channel Estimation Algorithms

The efficiency of data transmission over fading channels in orthogonal frequency division multiplexing (OFDM) systems depends on the employed interleaving method. In this study, we propose an improved chaotic interleaving scheme which aims to improve the performance of OFDM system under fading channel. In the proposed scheme, the binary data is interleaved with chaotic Baker map prior to the modulation process. In the sequel, significant degree of encryption is being added during data transmission. The performance of the proposed approach is tested on the conventional fast Fourier transform OFDM, discrete wavelet transform OFDM, and discrete cosine transform OFDM with and without chaotic interleaving. Furthermore, an expectation–maximization (EM) algorithm is proposed for improving channel impulse response (CIR) estimation based on a maximum likelihood principle. The proposed scheme makes use of EM algorithm to update the channel estimates until convergence is reached. The simulation results show the efficiency of the proposed algorithms under Rayleigh fading environments where the symbol error rate essentially coincides with that of the perfect channel case after the fifth EM iteration.     

Performance Analysis of Downlink W-CDMA Systems in Weibull and Lognormal Fading Channels Using Chaotic Codes

In this paper, a subspace based blind channel estimation scheme for downlink W-CDMA systems using chaotic codes under Weibull and Lognormal fading channel conditions is proposed and compared with W-CDMA system using PN codes. The algorithm provides estimates of multiuser channels by exploiting the structural information of the data output. The subspace of the (data + noise) matrix contains sufficient information for unique determination of channels and, hence, the signature waveforms and signal constellations. The proposed channel estimation algorithm is also implemented for multiuser—orthogonal frequency division multiplexing (OFDM) system. Performance measures like bit error rate (BER) and root mean square error (RMSE) are plotted for Weibull and Lognormal fading channels. Signal constellations under Weibull and Lognormal channels are also plotted. Analytical and Simulation results for BER and RMSE are compared for W-CDMA system using PN codes and chaotic codes. Simulation results show that, chaos-based W-CDMA outperforms the PN-based W-CDMA in terms BER and RMSE. Simulation results of multiuser-OFDM system shows that performance is further improved when compared to the W-CDMA system.     

Additive mixing modulation for public key encryption based on distributed dynamics

We introduce a public key encryption scheme that is based on additive mixing of a message with chaotic nonlinear dynamics. A high-dimensional dissipative nonlinear dynamical system is distributed between transmitter and receiver. The transmitter dynamics is public (known to all) and the receiver dynamics is private (known only to the authorized receiver). Bidirectional signals that couple transmitter and receiver are transmitted over a public channel. Once the chaotic dynamics which is initialized with a random state converges to the attractor, a message is mixed with the chaotic dynamics at the transmitter. The authorized receiver who knows the entire dynamics can use a simple algorithm to decode the message. An unauthorized receiver does not know the receiver dynamics and needs to use computationally unfeasible algorithms in order to decode the message. Security is maintained by altering the private receiver dynamics during transmission. We show that using additive mixing modulation is more efficient than the attractor position modulation distributed dynamics encryption scheme. We demonstrate the concept of this new scheme by simulating a simple coupled map lattice.     

Hybrid channel estimation for MIMO relay systems with Doppler offset influences

The design of the channel estimation method in a multiple‐input multiple‐output (MIMO) relay system plays a highly crucial role in deciding the overall system performance. For the realistic scenarios specifically, with fast time‐varying channel conditions due to highly mobile communicating nodes, the degree of accuracy to which the channel estimates are obtained for MIMO relay systems influences the communication system reliability significantly. However, most of the channel estimation approaches proposed in literature for MIMO relay systems assume that the Doppler offset contributed by highly mobile nodes is already known to the receiver, ignoring the resulting nonlinear system dynamics. Hence, a novel hybrid algorithm is proposed to address the issue of time‐varying channel estimation under fast‐fading channel condition with Doppler offset influences contributed by high‐mobility communicating nodes for a 1‐way 2‐hop MIMO amplify‐and‐forward relaying system. The problem is first formulated as a nonlinear state‐space model, and then an algorithm is developed to estimate the individual source‐to‐relay and relay‐to‐destination channels in the presence of the associated dynamic Doppler offset. In the proposed method, a set of superimposed orthogonal pilots is used for aiding in the updation of the channel gains, since Kalman filter–based updation may lead to accumulation of estimation and prediction error. A detailed computational complexity analysis of the proposed hybrid algorithm is presented, which shows that the algorithm has moderate computational complexity with a good performance in fast time‐varying channel conditions with high node mobility in a dual‐hop MIMO relay system.     

Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in \(2\times 1\) space–time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.     

Breaking Chaotic Direct Sequence Spreading Spectrum Signals Under the Multipath Fading Channel

In order to break chaotic direct sequence spreading spectrum (CD3S) signals under the multipath fading channel, a particle filter based algorithm combining blind channel equalization with chaos fitting is proposed. To implement this algorithm, the intruder substitutes a different chaotic equation into the state-space equations of the channel and the chaos fitting, and then multiple particle filters are used for blind channel equalization and chaos fitting simultaneously by implementing them in reciprocal interaction. As a result, the impact brought about by the multipath fading channel and additive noises can be overcome. Furthermore, the range-differentiating factor is used to make the inevitable chaos fitting error advantageous based on the chaos fitting method. Thus, the CD3S signals can be broken according to the range of the estimated message. Simulations show that the binary message signal can be extracted from the CD3S signals without any knowledge of the chaotic transmitter’s structure, parameters, initial value, or the channel characteristics.     

Channel estimation and long-range prediction of fast fading channels for adaptive OFDM system

This correspondence presents the channel estimation and long-range prediction technique for adaptive-orthogonal-frequency-division-multiplexing (AOFDM) system. The efficient channel loading is accomplished by feeding the accurately predicted channel-state-information (CSI) back to transmitter. The frequency-selective wireless fading channel is modelled as a tapped-delay-line-filter governed by a first-order autoregressive (AR1) process; and an adaptive channel estimator based on the generalised-variable-step-size least-mean-square (GVSS-LMS) algorithm tracks AR1 correlation coefficient. To compensate for the signal fading due to channel state variations, a modified-Kalman-filter (MKF)-based channel estimator is utilised. In addition, channel tracking is also performed for predicting future CSI at receiver, based on the numeric-variable-forgetting-factor recursive-least-squares (NVFF-RLS) algorithm. Subsequently, adaptive bit allocation for AOFDM system is employed by using predicted CSI at transmitter. Here, the proposed combination of GVSS-LMS and MKF algorithms for robust channel estimation and the NVFF-RLS algorithm for efficient channel prediction is incorporated. The performance validation of presented method is carried out by using different channel realisations through simulation, and also by comparing it with fixed step-size LMS, MKF and fixed forgetting-factor RLS algorithm based conventional techniques. Eventually, the reliable performance of underlying AOFDM system can be achieved in terms of the lower mean squared estimation/prediction errors and alleviated symbol error rate.