基于频域插值的信道化原型滤波器设计

针对信道化原型滤波器设计复杂度较高的问题,提出一种满足线性相位要求的原型滤波器频域插值设计算法。该算法采用无约束迭代优化办法获得了阻带衰减较高的低阶滤波器模型,然后根据所推导的频域插值模型构建满足线性相位特性的原型滤波器频谱,最后通过快速傅里叶逆变换获得了满足信道化滤波器组完全重构特性要求的原型滤波器。所提设计算法将频域插值与迭代优化相结合,有效地解决了设计高阻带衰减的原型滤波器时待优化参数数目较大的问题,并且不会产生镜像频谱,省去了镜像抑制滤波器的设计。仿真结果表明,所提算法相比传统的时域插值设计算法具有显著的性能改善,并且降低了设计复杂度。     

对色散信道的预编码-判决反馈均衡联合最优设计

本文提出了利用滤波器组预编码及判决反馈均衡对色散信道实现收/发联合最优设计的方法.该方法以最大互信息量为设计准则,在固定发送功率约束条件下得到了新的设计结果.证明了预编码器使频率选择性信道等价为一组独立的平坦衰落子信道,且后向反馈滤波器等价为一条直通子信道,结论表明系统各子信道的信噪比与采用线性均衡结构的结果相比有所增加,从而使系统具有更低的误码率性能.文章通过仿真验证了该结论     

线性相位余弦调制滤波器组研究

图像子带编码要求滤波器组具有线性相位特性，非线性相位特性可对图像编译码带来影响。现研究了一种具有线性相位的最大抽取FIR余弦调制滤波器组，该滤波器组中每一个滤波器都具有线性相位，且由原型滤波器经余弦序列调制得到。经推导这种滤波器组可以设计成具有近似准确重构（NPR）特性或准确重构（PR）特性，文中分别给出了两种情况下滤波器组应满足的条件。通过对一幅图像的二维可分离滤波实验说明了当原型滤波器满足一定条件时滤波器组是准确重构的。     

双/多基地综合脉冲孔径地波雷达的信道化接收技术研究

双/多基地综合脉冲孔径地波雷达在发射站为多载频信号同时辐射,在接收站综合形成发射方向图之前需要对各发射信号分量进行分离.尽管采用数字混频和低通滤波器组可以对各发射分量进行分离,但运算量太大,难以实时实现.为此提出采用多相滤波器组信道化接收技术,设计该雷达的多相滤波器组信道化接收机.这种多相滤波器组信道化接收机与低通滤波器组信道化接收相比,运算量减少N²倍(N为信道数).最后给出了该雷达试验系统的实测数据处理结果,验证了这种多相滤波器组信道化接收技术在该雷达信号处理中的有效性.     

具有近似线性相位特性的多相波数字滤波器的设计

不考虑相位失真,而要求满足比较苛刻的振幅特性的时候,和FIR滤波器比较起来,采用 IIR 滤波器,计算量要小很多.但对某些IIR滤波器,采取适当的方法,也可以使其相位失真变得很小.本文提出一种新的设计方法,用来设计振幅响应完全满足设计要求,而相位特性用Chebyshev 近似来实现的多相波数字滤波器.这种方法的关键在于确定通带中的若干衰减零点.文中对需要的最小衰减零点数作了估计.实例表明,用这种方法设计的滤波器,时延小,相位特性非常接近线性.     

数字微波通信系统传输特性的一种设计方法

对于一个数字微波通信系统,传输特性是最重要的问题之一,必须慎重加以考虑。本文讨论了控制总传输特性的因素,结果选定了 N=6的最大平坦特性。这个选定的响应,可由设计的各种滤波器包括微波发送、微波接收、微波收发信分路及中频滤波器来实现。设计时假设信道是线性的。最后给出了最优化的设计方法和设计结果。     

基于NPR调制滤波器组的动态信道化滤波

针对接收带宽内存在多个非均匀分布的、不同带宽的子带信号,其个数、带宽和位置分布未知且具有时变性的情况,本文提出一种动态信道化滤波的方法:首先由给定的信号间的最小保护间隔确定滤波器组的子信道数目并设计满足几乎完全重构条件的原型滤波器以构成分析滤波器组;其次,通过计算每个子信道的能量确定信号的位置,并设计相应的综合滤波器组提取子带信号;当信号动态变化时,无需改变分析滤波器组,只要更新能量检测以获取新信号的位置信息就能得到相应的综合滤波器组从而完成信号的信道化处理.理论分析和仿真结果验证了该方法的可行性.     

用DSP实现FIR数字滤波器

FIR滤波器具有幅度特性可随意设计、线性相位特性可严格精确保证等优点,因此在要求相位线性信道的现代电子系统,如图像处理、数据传输等波形传递系统中,具有很大吸引力。本文简单介绍了其线性相位条件和设计方法,并且提供了一种用DSP实现的方法。     

基于四进制频移键控调制的无线光通信同态滤波技术研究

基于无线光通信链路噪声特性,建立了大气信道等效数学模型。针对大气湍流引起的信号起伏,对通过大气信道的四进制频移键控(4FSK)已调信号采用了同态滤波技术去噪,同态系统中线性滤波器设计了零相位数字滤波器。当信源码字长度取500时,同态滤波去噪后的信号通过过零检测进行解调,在光强闪烁指数s2σ=0.1,信噪比10 dB时,误码率达到7×10-4,滤波效果明显优于传统数字滤波器。仿真结果表明,同态滤波能有效地滤除大气信道中的乘性噪声,能够有效地抑制大气湍流,提高光通信可靠性。     

射频识别阅读器中信道选择滤波器的设计

基于IBM0.18μm标准CMOS工艺,设计了一种适用于UHF RFID(Radio Frequency Identification)阅读器的信道选择滤波器。这种滤波器和其他结构滤波器相比,可以获得更高的线性度和更好的噪声特性。设计中,低通滤波器截止频率0.3~1.3 MHz范围内可调。当截止频率设置为900 kHz时,带内增益稳定在0 dB,在1.8 MHz频率处具有大于49 dB的幅度衰减。     

Restoration of Noisy Multispectral Images with a Geodetic Distance Filter

A number of algorithms for image filtering based on the geodetic distance kernel have been proposed recently. When applied to noisy images, some of them yield restoration results that are comparable to those provided by the best modern algorithms. An algorithm of this class that improves the restoration result for multispectral images is proposed. The algorithm uses a convolution kernel based on the geodetic distance and offers a number of advantages, since it allows for recursive computation and, consequently, rapid image processing. The quality of image restoration by the proposed algorithm increases as a function of the number of image channels, which is important for restoration of multispectral images. Several criteria are used to estimate the quality of image restoration.     

Channel Equalization Using Adaptive Lattice Algorithms

In this paper, a study of adaptive lattice algorithms as applied to channel equalization is presented. The orthogonalization properties of the lattice algorithms make them appear promising for equalizing channels which exhibit heavy amplitude distortion. Furthermore, unlike the majority of other orthogonalization algorithms, the number of operations per update for the adaptive lattice equalizers is linear with respect to the number of equalizer taps.     

Efficient order recursive algorithms for multichannel least squaresfiltering

Four efficient order-recursive algorithms for least-squares (LS) multichannel FIR filtering and multivariable system identification are developed. The need for such algorithms arises when the system model assigns an unequal number of delay elements to each input channel. All proposed schemes provide considerable improvements over overparametrization or the zero padding approach. First, a block-structures algorithm is derived. It operates on boxes, or blocks, whose dimensions successively increase until their size equals the number of input channels. As a result, it requires linear system solvers and matrix multiplications. The second algorithm manages to get free of block operations by proper decomposition of each block step involved in the first method into a number of scalar steps equal to the size of the block. The third and the fourth algorithms provide highly concurrent alternatives that reduce processing time by an order magnitude. An illustrative example from multichannel autoregressive spectral estimation is supplied     

Low-Complexity Block Turbo Equalization for OFDM Systems in Time-Varying Channels

We propose low-complexity block turbo equalizers for orthogonal frequency-division multiplexing (OFDM) systems in time-varying channels. The presented work is based on a soft minimum mean-squared error (MMSE) block linear equalizer (BLE) that exploits the banded structure of the frequency-domain channel matrix, as well as a receiver window that enforces this banded structure. This equalization approach allows us to implement the proposed designs with a complexity that is only linear in the number of subcarriers. Three block turbo equalizers are discussed: two are based on a biased MMSE criterion, while the third is based on the unbiased MMSE criterion. Simulation results show that the proposed iterative MMSE BLE achieves a better bit error rate (BER) performance than a previously proposed iterative MMSE serial linear equalizer (SLE). The proposed equalization algorithms are also tested in the presence of channel estimation errors.      

Reduced-state BCJR-type algorithms

We propose a technique to reduce the number of trellis states in BCJR-type algorithms, i.e., algorithms. with a structure similar to that of the well-known algorithm by Bahl, Cocke, Jelinek, and Raviv (1974). This work is inspired by reduced-state sequence detection (RSSD). The key idea is the construction, during one of the recursions in the reduced-state trellis, of a “survivor map” to be used in the other recursion. In a more general setting, two distinct survivor maps could be determined in the two recursions and used jointly to approximate the a posteriori probabilities. Three examples of application to iterative decoding are shown: (1) coherent detection for intersymbol interference (ISI) channels; (2) noncoherent detection based on an algorithm previously proposed by the authors; and (3) detection based on linear prediction for Rayleigh fading channels. As in classical RSSD, the proposed algorithm allows significant state-complexity reduction with limited performance degradation     

Adaptive algorithms for sparse system identification

In this paper, identification of sparse linear and nonlinear systems is considered via compressive sensing methods. Efficient algorithms are developed based on Kalman filtering and Expectation-Maximization. The proposed algorithms are applied to linear and nonlinear channels which are represented by sparse Volterra models and incorporate the effect of power amplifiers. Simulation studies confirm significant performance gains in comparison to conventional non-sparse methods.     

Minimum Interference Channel Assignment in Multiradio Wireless Mesh Networks

In this paper, we consider multi-hop wireless mesh networks, where each router node is equipped with multiple radio interfaces and multiple channels are available for communication. We address the problem of assigning channels to communication links in the network with the objective of minimizing overall network interference. Since the number of radios on any node can be less than the number of available channels, the channel assignment must obey the constraint that the number of different channels assigned to the links incident on any node is atmost the number of radio interfaces on that node. The above optimization problem is known to be NP-hard. We design centralized and distributed algorithms for the above channel assignment problem. To evaluate the quality of the solutions obtained by our algorithms, we develop a semidefinite program and a linear program formulation of our optimization problem to obtain lower bounds on overall network interference. Empirical evaluations on randomly generated network graphs show that our algorithms perform close to the above established lower bounds, with the difference diminishing rapidly with increase in number of radios. Also, ns-2 simulations as well as experimental studies on testbed demonstrate the performance potential of our channel assignment algorithms in 802.11-based multi-radio mesh networks.     

Determination of Tap Positions for Sparse Equalizers

Sparse equalizers, in which only a small subset of the filter taps is selected to be nonzero, were recently proposed as a low-complexity solution for receivers operating in wireless frequency-selective channels with sparse power profiles. The performance of the sparse equalizer heavily depends on its tap-positioning algorithm. This paper presents efficient low-complexity algorithms for determination of sparse equalizer tap positions based on a forward sequential search. We develop low-complexity metrics for the evaluation of the candidate tap positions in the search space as well as methods to effectively reduce the search space size. The proposed algorithms are shown to be superior over previously proposed algorithms in a wide range of channel conditions. Actually, the proposed algorithms yield, in most of the tested cases, performance identical to the optimal, prohibitively complex, tap-positioning algorithm. The main emphasis is on linear equalization suitable for wideband code-division multiple-access systems but the algorithm can be extended to a variety of equalization schemes and channels.     

一种新型的基于神经网络的无线信道模型

本文提出了一种使用神经网络来建模和仿真无线通信信道的方法。由于神经网络优异的学习特性,文中使用神经网络对无线信道的输入输出响应进行建模,讨论了建模的方法,给出了仿真的结果,分析了遇到的问题和解决方法,并进一步阐述了在实际的无线信道环境中使用神经网络建模的优越性。