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     

Semiblind channel estimation for pulse-based ultra-wideband wireless communication systems

This paper proposes an H/sub /spl infin// based semiblind channel estimation algorithm for pulse-based ultra-wideband (UWB) wireless communication systems. In the proposed scheme, sparsely inserted periodic pilot symbols are exploited to adapt to not only the time-varying channel fading and noise processes but to their changing statistics and potential external disturbances, such as interference. While the existing optimal filtering-based channel estimation schemes, which are optimized mostly for traditional narrowband or wideband systems, require a priori knowledge of the channel and noise statistics, the proposed scheme does not. By further making full use of the channel characteristics unique in UWB systems, the proposed method is thus especially useful for robust operation in the highly frequency-selective UWB indoor channels for which the channel statistics are environment-dependent, and the noise processes do not necessarily satisfy the white Gaussian distribution in the presence of potential narrowband and multiuser interferences. Performance gain of the proposed scheme over the least square method, an existing technique that could also be applied to UWB channels with unknown statistics, and the Wiener filter-based algorithm is also provided.     

Robust vector quantization by a linear mapping of a block code

In this paper we propose a novel technique for vector quantizer design where the reconstruction vectors are given by a linear mapping of a binary block code (LMBC). The LMBC framework provides a relation between the index bits and the reconstruction vectors through mapping properties. We define a framework, show its flexibility, and give optimality conditions. We consider source optimized vector quantization (VQ), where the objective is to directly obtain a VQ with inherent good channel robustness properties. Several instructive theoretical results and properties of the distortion experienced due to channel noise are demonstrated. These results are used to guide the design process. Both optimization algorithms and a block code selection procedure are devised. Experimental results for Gauss-Markov sources show that quantization performance close to an unconstrained VQ is obtained with a short block code which implies a constrained VQ. The resulting VQs have better channel noise robustness than conventional VQs designed with the generalized Lloyd algorithm (GLA) and splitting initialization, even when a post-processing index assignment algorithm is applied to the GLA-based VQ. We have, thus, demonstrated a unique method for direct design resulting in an inherent good index assignment combined with small losses in quantization performance     

A soft decoding scheme for vector quantization over a CDMA channel

The optimal decoding of vector quantization (VQ) over a code-division multiple-access (CDMA) channel is too complicated for systems with a medium-to-large number of users. This paper presents a low-complexity, suboptimal decoder for VQ over a CDMA channel. The proposed decoder is built from a soft-output multiuser detector, a soft bit estimator, and the optimal soft VQ decoding of an individual user. Simulation results obtained over both additive white Gaussian noise and flat Rayleigh fading channels show that with a lower complexity and good performance, the proposed decoding scheme is an attractive alternative to the more complicated optimal decoder.     

Recursive blind identification of non-Gaussian time-varying ARmodel and application to blind equalisation of time-varying channel

A novel method for the blind identification of a non-Gaussian time-varying autoregressive model is presented. By approximating the non-Gaussian probability density function of the model driving noise sequence with a Gaussian-mixture density, a pseudo maximum-likelihood estimation algorithm is proposed for model parameter estimation. The real model identification is then converted to a recursive least squares estimation of the model time-varying parameters and an inference of the Gaussian-mixture parameters, so that the entire identification algorithm can be recursively performed. As an important application, the proposed algorithm is applied to the problem of blind equalisation of a time-varying AR communication channel online. Simulation results show that the new blind equalisation algorithm can achieve accurate channel estimation and input symbol recovery     

基于粒子滤波的MIMO-OFDM时变信道半盲估计

提出一种基于粒子滤波的MIMO-OFDM时变信道半盲估计方法。首先，对粒子滤波算法进行改进，通过对采样粒子分布进行局部优化调整，提出一种局部优化粒子滤波算法。然后，将该粒子滤波算法用于MIMO-OFDM时变信道估计。由于该信道估计过程在频域进行，因而无需已知（或估计）多径信道长度。与现有时变信道半盲估计方法相比，本方法具有估计误差低、对非高斯噪声顽健性强等特点，从而有效改善了接收端的符号检测性。计算机仿真结果证明了本方法的有效性。     

Joint optimization of VQ codebooks and QAM signal constellationsfor AWGN channels

A joint design scheme has been proposed to optimize the source encoder and the modulation signal constellation based on the minimization of the end-to-end distortion including both the quantization error and channel distortion. The proposed scheme first optimizes the vector quantization (VQ) codebook for a fixed modulation signal set, and then the modulation signals for the fixed VQ codebook. These two steps are iteratively repeated until they reach a local optimum solution. It has been shown that the performance of the proposed system can be enhanced by employing a new efficient mapping scheme between codevectors and modulation signals. Simulation results show that a jointly optimized system based on the proposed algorithms outperforms the conventional system based on a conventional quadrature amplitude modulation signal set and the VQ codebook designed for a noiseless channel     

基于最优导频的自适应Kalman滤波信道估计方法

针对时变信道中的子载波间干扰(ICI)和噪声的统计模型不准确引起的滤波发散问题,介绍了一种基于最优导频预滤波的自适应Kalman联合算法。该算法通过使用最优导频滤除ICI,获得理想信道初始状态,然后将其作为Kalman滤波初始信息在时域上进行自适应Kalman信道估计。最后仿真实验表明,和传统的基于导频的Kalman滤波(KF)算法相比,该方法能有效抑制KF发散和改善信道估计精度。     

Lagrangean Based Methods for Solving Large-Scale Cellular Network Design Problems

The cellular network design (CND) problem is formulated as a comprehensive linear mixed integer programming model integrating the base station location (BSL) problem, the frequency channel assignment (FCA) problem and the topological network design (TND) problem. A solution algorithm based on Lagrangean relaxation is proposed for solving this complex cellular network design problem. Pursuing the optimum solution through exact algorithms to this problem appears to be unrealistic considering the large scale nature and NP-hardness of the problem. Therefore, the solution algorithm strategy consists in computing effective lower and upper bounds for the problem. Lower bounds are evaluated through a Lagrangean relaxation technique and subgradient method. A Lagrangean heuristic is developed to compute upper bounds based on the Lagrangean solution. The bounds are improved through a customized branch and bound algorithm which takes in account specific knowledge of the problem to improve its efficiency. Thirty two random test instances are solved using the proposed algorithm and the CPLEX optimization package. The results show that the duality gap is excessive, so it cannot guarantee the quality of the solution. However, the proposed algorithm provides optimal or near optimal solutions for the problem instances for which CPLEX also provides the optimal solution. It further suggests that the proposed algorithm provides optimal or near optimal solutions for the other instances too. Finally, the results demonstrate that the proposed algorithm is superior to CPLEX as a solution approach for the CND problem.     

Optimal time-frequency deconvolution filter design fornonstationary signal transmission through a fading channel: AF filterbank approach

The purpose of this paper is to develop a new approach-time-frequency deconvolution filter-to optimally reconstruct the nonstationary (or time-varying) signals that are transmitted through a multipath fading and noisy channel. A deconvolution filter based on an ambiguity function (AF) filter bank is proposed to solve this problem via a three-stage filter bank. First, the signal is transformed via an AF analysis filter bank so that the nonstationary (or time-varying) component is removed from each subband of the signal. Then, a Wiener filter bank is developed to remove the effect of channel fading and noise to obtain the optimal estimation of the ambiguity function of the transmitted signal in the time-frequency domain. Finally, the estimated ambiguity function of the transmitted signal in each subband is sent through an AF synthesis filter bank to reconstruct the transmitted signal. In this study, the channel noise may be time-varying or nonstationary. Therefore, the optimal separation problem of multicomponent nonstationary signals is also solved by neglecting the transmission channel     

Particle filtering based channel estimation in OFDM power line communication

Particle filtering （PF） algorithm has the powerful potential for coping with difficult non-linear and non-Gaussian problems. Aiming at non-linear, non-Gaussian and time-varying characteristics of power line channel, a time-varying channel estimation scheme combined PF algorithm with decision feedback method is proposed. In the proposed scheme, firstly the indoor power line channel is measured using the pseudo-noise （PN） correlation method, and a first-order dynamic autoregressive （AR） model is set up to describe the measured channel, then, the channel states are estimated dynamically from the received signals by exploiting the proposed scheme. Meanwhile, due to the complex noise distribution of power line channel, the performance of channel estimation based on the proposed scheme under the Middleton class A impulsive noise environment is analyzed. Comparisons are made with the channel estimation scheme respectively based on least square （LS）, Kalman filtering （KF） and the proposed algorithm. Simulation indicates that PF algorithm dealing with this power line channel estimation difficult non-linear and non-Gaussian problems performance is superior to those of LS and KF respectively, so the proposed scheme achieves higher estimation accuracy. Therefore, it is confirmed that PF algorithm has its own unique advantage for power line channel estimation.     

A new vector quantization technique for performance evaluation infrequency hopping mobile radio systems

A new technique based on vector quantization (VQ) is proposed for interfacing the link-level and the system level analysis of frequency hopping (FH) mobile systems. The method represents an original solution to overcome the problem that the time scales adopted within the two analysis levels are usually different, thus discouraging a direct link between them. The VQ technique is applied, as a test case, to the study of a typical FH-GSM mobile environment. The corresponding results show excellent correlation with those achievable by means of a much more time consuming, as well as often infeasible, direct approach     

Fast Time-Varying Dispersive Channel Estimation and Equalization for an 8-PSK Cellular System

In this paper, a novel channel-estimation scheme for an 8-PSK enhanced data rates for GSM evolution (EDGE) system with fast time-varying and frequency-selective fading channels is presented. Via a mathematical derivation and simulation results, the channel impulse response (CIR) of the fast fading channel is modeled as a linear function of time during a radio burst in the EDGE system. Therefore, a least-squares-based method is proposed along with the modified burst structure for time-varying channel estimation. Given that the pilot-symbol blocks are located at the front and the end of the data block, the LS-based method is able to estimate the parameters of the time-varying CIR accurately using a linear interpolation. The proposed time-varying estimation algorithm does not cause an error floor that existed in the adaptive algorithms due to a nonideal channel tracking. Besides, the time-varying CIR in the EDGE system is not in its minimum-phase form, as is required for low-complexity reduced-state equalization methods. In order to maintain a good system performance, a Cholesky-decomposition method is introduced in front of the reduced-state equalizer to transform the time-varying CIR into its minimum-phase equivalent form. Via simulation results, it is shown that the proposed algorithm is very well suited for the time-varying channel estimation and equalization, and a good bit-error-rate performance is achieved even at high Doppler frequencies up to 300 Hz with a low complexity.     

On-line blind multichannel equalization based on mutually referenced filters

This paper presents a novel approach to the blind linear equalization of possibly nonminimum phase and time-varying communication channels. In the context of channel diversity, we introduce the concept of mutually referenced equalizers (MREs) in which several filters are considered, the outputs of which act as training signals for each other. A corresponding (constrained) multidimensional mean-square error (MSE) cost function is derived, the minimization of which is shown to be a necessary and sufficient condition for equalization. The links with a standard linear prediction problem are demonstrated. The proposed technique exhibits properties of important practical concern: 1) the proposed algorithm is globally convergent. 2) Simple closed-form solutions exist for the MREs, but the MREs also lend themselves readily to adaptive implementation. In particular, the recursive least-squares (RLS) algorithm can be used to offer optimal convergence rate. 3) The MRE method provides a solution for all equalization delays, which results in robustness properties with respect to SNR and ill-defined channel lengths.     

Regularized subband coding scheme

Two enhanced subband coding schemes using a regularized image restoration technique are proposed: the first controls the global regularity of the decompressed image; the second extends the first approach at each decomposition level. The quantization scheme incorporates scalar quantization (SQ) and pyramidal lattice vector quantization (VQ) with both optimal bit and quantizer allocation. Experimental results show that both the block effect due to VQ and the quantization noise are significantly reduced.     

Fuzzy channel-optimized vector quantization

A novel fuzzy clustering algorithm for the design of channel-optimized source coding systems is presented in this letter. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, optimizes the vector quantizer (VQ) design using a fuzzy clustering process in which the index crossover probabilities imposed by a noisy channel are taken into account. The fuzzy clustering process effectively enhances the robustness of the performance of VQ to channel noise without reducing the quantization accuracy. Numerical results demonstrate that the FCOVQ algorithm outperforms existing VQ algorithms under noisy channel conditions for both Gauss-Markov sources and still image data     

MIMO系统中改进的二进制粒子群天线选择算法

在多输入多输出系统中,发射端和接收端的多天线配置提高了信道容量和传输可靠性,而天线选择技术能在保持系统优点的同时有效地降低运算复杂度以及硬件成本。为了能在时变的信道条件下快速地选择出一组最优的天线子集,提出了一种基于二进制粒子群算法的改进的天线选择算法。推导出了二进制粒子群联合收发端天线选择的信道容量公式,并将其作为粒子群算法的适应度函数,使天线选择问题转换成二进制编码串的组合优化问题。通过改进模糊函数提高粒子群算法的收敛性,让二进制粒子群尽可能地收敛于全局最优位置。仿真结果表明,改进的算法能在降低运算复杂度的同时提高收敛性,且系统信道容量趋近于最优算法。     

基于凸优化方法的认知雷达波形设计

针对认知雷达扩展目标检测的问题,提出了一种与目标散射特性相关的相位编码信号设计方法,利用半正定松弛将输出信噪比的优化问题松弛为一个凸优化问题,并利用一维交互迭代搜索逼近原问题的全局最优解。该方法具有收敛速度块、运算量小等优点,能够准确逼近全局最优解。     

Joint Data Detection and Channel Tracking for OFDM Systems With Phase Noise

This paper addresses the problem of data detection in orthogonal frequency division multiplexing (OFDM) systems operating under a time-varying multipath fading channel. Optimal detection in such a scenario is infeasible, which makes the introduction of approximations necessary. The typical joint data-channel estimators are decision directed, that is, assume perfect past data decisions. However, their performance is subject to error propagation phenomena. The variational Bayes method is employed here, which approximates the joint data and channel distribution as a separable one, greatly simplifying the problem. The data detection part of the resulting algorithm provides soft data estimates that are used for channel tracking. The channel itself is modeled as an autoregressive process allowing for a Kalman-like tracking algorithm. According to the developed algorithm, both data and channel estimates are exchanged and updated in an iterative manner. The performance of the proposed algorithm is evaluated by simulations. Furthermore, since OFDM is extremely sensitive to the presence of phase noise, the algorithm is extended to operate under severe phase noise conditions, with moderate performance degradation.      

Robust transmission based on variable-rate error control andgenetic programming

This paper presents a novel variable-rate error control design algorithm matched to full-search vector quantizers (VQs) for robust transmission. In the algorithm, different locations of binary strings obtained from VQ encoders are protected by channel codes with different protection levels. The degree of protection at each location is determined by a genetic programming technique minimizing the end-to-end average distortion of transmission systems. The technique outperforms the equal error protection method. Moreover, as compared with a full search algorithm for optimal unequal error protection, our technique attains comparable performance with significantly lower computational complexities