利用自适应均衡滤波器改善大气湍流信道OWC性能

光无线通信是近年来无线通信领域的研究热点之一。大气湍流是影响光无线通信的重要因素,特别是在高速数字通信中会产生严重的码间干扰。本文分析了大气湍流信道的特性,提出采用自适应LMS均衡技术改善大气湍流信道的性能,对采用OOK调制方式的OWC系统均衡前后的误码率性能进行了分析比较。仿真结果表明自适应LMS均衡技术可以将系统的性能提高约10dB。     

直接检测的光正交频分复用传输系统最小均方自适应算法

为了减少直接检测的光正交频分复用(DD-OOFDM)传输系统中色散对系统的影响,传输系统使用了基于频域的最小均方(LMS)自适应均衡技术,由于基于频域的LMS估计方法计算复杂度低且便于信号块处理,相比最小平方(LS)估计方法,可更有效地追踪信道变化,减小相位噪声对传输系统的影响。实验结果表明,经背靠背(BTB)和100km标准单模光纤(SSMF)传输后,使用频域LMS估计方法的信号比使用频域LS估计方法的信号系统接收功率代价在误码率为10×10-2.5和10×10-2.0时分别降低了2dB及2.5dB,频域LMS估计方法比频域LS估计方法对传输系统具有更好的色散补偿效果。     

An adaptive algorithm for differentially coherent detection in the presence of intersymbol interference

An adaptive equalization method is proposed for use with differentially coherent detection of M-ary differential phase-shift keying (DPSK) signals in the presence of unknown carrier frequency offset. A decision-feedback or a linear equalizer is employed, followed by the differentially coherent detector. The equalizer coefficients are adjusted to minimize the post-detection mean squared error. The error, which is a quadratic function of the equalizer vector, is used to design an adaptive algorithm of stochastic gradient type. The approach differs from those proposed previously, which linearize the post-detection error to enable the use of least mean squares (LMS) or recursive least squares (RLS) adaptive equalizers. The proposed quadratic-error (Q) algorithm has complexity comparable to that of LMS, and equal convergence speed. Simulation results demonstrate performance improvement over methods based on linearized-error (L) algorithm. The main advantages of the technique proposed are its simplicity of implementation and robustness to carrier frequency offset, which is maintained for varying modulation level.     

Application of Sequential Estimation Algorithm Based on Branch Metric to Frequency-Selective Channel Equalization

In this work, a sequential estimation algorithm based on branch metric is used as channel equalizer to combat intersymbol interference in frequency-selective wireless communication channels. The bit error rate (BER) and computational complexity of the algorithm are compared with those of the maximum likelihood sequence estimation (MLSE), the recursive least squares (RLS) algorithm, the Fano sequential algorithm, the stack sequential algorithm, list-type MAP equalizer, soft-output sequential algorithm (SOSA) and maximum-likelihood soft-decision sequential decoding algorithm (MLSDA). The BER results have shown that whilst the sequential estimation algorithm has a close performance to the MLSE using the Viterbi algorithm, its performance is better than the other algorithms. Beside, the sequential estimation algorithm is the best in terms of computational complexity among the algorithms mentioned above, so it performs the channel equalization faster. Especially in M-ary modulated systems, the equalization speed of the algorithm increases exponentially when compared to those of the other algorithms.     

Optimization of Recursive Least Square-Based Adaptive Linear Equalizer for ZigBee Transceiver

An efficient technique to compensate for the channel detrimental effects in ZigBee systems is introduced in this paper. The proposed methodology relies on adding a recursive least square (RLS) based adaptive linear equalizer (ALE) to the physical layer of the receiver side. The performance of the RLS based ALE is investigated inside the ZigBee system under different multipath fading situations: Rician and Rayleigh. Moreover, the paper proposes a methodology for deciding the RLS based ALE’s design parameters. The design procedure depends on solving multiple objectives optimizing function based on genetic algorithms (GAs). The ALE’s parameters are chosen, such that the system experiences minimum bit error rate (BER) with fast convergence response. For design verification purposes, the ZigBee transceiver is modeled in MATLAB Simulink and tested under different fading and signal to noise ratios. In addition, the performance of the RLS adaptation algorithm is compared with the least mean square (LMS) one. The results show that the RLS based ALE provides better ZigBee performance with less BER and fast adaptation response.     

Adaptive linear equalization combined with noncoherent detectionfor MDPSK signals

A novel noncoherent receiver for M-ary differential phase-shift keying signals transmitted over intersymbol interference channels is presented. The noncoherent receiver consists of a linear equalizer and a decision-feedback differential detector. A significant performance gain over a previously proposed noncoherent receiver can be observed. For an infinite number of feedback symbols, the optimum equalizer coefficients can be calculated analytically, and the performance of the proposed receiver approaches that of a coherent linear minimum mean-squared-error equalizer. Moreover, a modified least mean square and a modified recursive least squares algorithm for adaptation of the equalizer coefficients are discussed     

Performance analysis of adaptive DFE using set-membership binormalized data-reusing LMS algorithm for frequency selective MIMO channels

This paper addresses the concern of complexity involved with adaptive equalization in wireless systems operating over time-varying and frequency selective multiple-input multiple-output (MIMO) channels. Here, we propose a decision feedback equalizer using binormalized data-reusing least mean square (BNLMS) algorithm with set-membership filtering for MIMO channels. The performance of the equalizer is investigated for a MIMO receiver in a multi-path fading environment as experienced in the indoor and pedestrian environment. The equalizer performance is also studied for channels having higher delay and Doppler spread. The convergence issues, BER performance and tracking capabilities are examined through computer simulations. Moreover, the computational complexity issue for this MIMO equalizer is compared with other existing data-selective algorithm based techniques.     

Application of Least Squares Lattice Algorithms to Adaptive Equalization

In many applications of adaptive data equalization, rapid initial convergence of the adaptive equalizer is of paramount importance. Apparently, the fastest known equalizer adaptation algorithm is based on a recursive least squares estimation algorithm. In this paper we show how the least squares lattice algorithms, recently introduced by Morf and Lee, can be adapted to the equalizer adjustment algorithm. The resulting algorithm, although computationally more complex than certain other equalizer algorithms (including the fast Kalman algorithm), has a number of desirable features which should prove useful in many applications.     

Adaptive frequency-domain equalization and diversity combining forbroadband wireless communications

We introduce a new kind of adaptive equalizer that operates in the spatial-frequency domain and uses either least mean square (LMS) or recursive least squares (RLS) adaptive processing. We simulate the equalizer's performance in an 8-Mb/s quaternary phase-shift keying (QPSK) link over a frequency-selective Rayleigh fading multipath channel with ~3 μs RMS delay spread, corresponding to 60 symbols of dispersion. With the RLS algorithm and two diversity branches, our results show rapid convergence and channel tracking for a range of mobile speeds (up to ~100 mi/h). With a mobile speed of 40 mi/h, for example, the equalizer achieves an average bit error rate (BER) of 10 ^-4 at a signal-to-noise ratio (SNR) of 15 dB, falling short of optimum linear receiver performance by about 4 dB. Moreover, it requires only ~50 complex operations per detected bit, i.e., ~400 M operations per second, which is close to achievable with state-of-the-art digital signal processing technology. An equivalent time-domain equalizer, if it converged at all, would require orders-of-magnitude more processing     

Least squares approach to blind channel equalization

We present a least squares (LS) algorithm for blind channel equalization based on a reformulation of the Godard algorithm. A transformation for the equalizer parameters is considered to convert the nonlinear LS problem inherent in the Godard algorithm to a linear LS problem. Unlike the Godard (1980) algorithm, the proposed LS approach does not suffer from ill-convergence to closed-eye local minima. Methods for extracting the equalizer parameters from their transformed version are developed. Offline and recursive implementations of the LS algorithm are presented. The algorithm requires only a small number of channel output observations to estimate the equalizer parameters and is therefore fast vis-a-vis the Godard algorithm. The channel input correlation does not impose any restriction on the application of the algorithm, as long as a weak sufficient-excitation condition is satisfied. Simulation examples are presented to demonstrate the LS approach and to compare it with the Godard algorithm     

Low-complexity adaptive decision-feedback equalization of MIMO channels

A new adaptive MIMO channel equalizer is proposed based on adaptive generalized decision-feedback equalization and ordered-successive interference cancellation. The proposed equalizer comprises equal-length subequalizers, enabling any adaptive filtering algorithm to be employed for coefficient updates. A recently proposed computationally efficient recursive least squares algorithm based on dichotomous coordinate descents is utilized to solve the normal equations associated with the adaptation of the new equalizer. Convergence of the proposed algorithm is examined analytically and simulations show that the proposed equalizer is superior to the previously proposed adaptive MIMO channel equalizers by providing both enhanced bit error rate performance and reduced computational complexity. Furthermore, the proposed algorithm exhibits stable numerical behavior and can deliver a trade-off between performance and complexity.     

Combined RLS-LMS initialization for per tone equalizers in DMT-receivers

In discrete multitone receivers, the classical equalizer structure consists of a (real) time domain equalizer (TEQ) combined with complex one-tap frequency domain equalizers. An alternative receiver is based on a per tone equalization (PTEQ), which optimizes the signal-to-noise ratio (SNR) on each tone separately and, hence, the total bitrate. In this paper, a new initialization scheme for the PTEQ is introduced, based on a combination of least mean squares (LMS) and recursive least squares (RLS) adaptive filtering. It is shown that the proposed method has only slightly slower convergence than full square-root RLS (SR-RLS) while complexity as well as memory cost are reduced considerably. Hence, in terms of complexity and convergence speed, the proposed algorithm is in between LMS and RLS.     

Multichannel MLSE equalizer with parametric FIR channelidentification

We propose a parametric finite impulse response (FIR) channel identification algorithm, apply the algorithm to a multichannel maximum likelihood sequential estimation (MLSE) equalizer using multiple antennas, and investigate the improvement in the overall bit error rate (BER) performance. By exploring the structure of the specular multipath channels, we are able to reduce the number of channel parameters to provide a better channel estimate for the MLSE equalizer. The analytic BER lower bounds of the proposed algorithm as well as those of several other conventional MLSE algorithms in the specular multipath Rayleigh-fading channels are derived. In the derivation, we consider the channel mismatch caused by the additive Gaussian noise and the finite-length channel approximation error. A handy-to-use simplified BER lower bound is also derived. Simulation results that illustrate the BER performance of the proposed algorithm in the global system for mobile communications (GSM) system are presented and compared to the analytic lower bounds     

100-Mbit/s single-chip Q-VLMS MLSE equalizer LSI for TDMA mobileradio communications

A single-chip 100-Mbit/s burst-operation two-tap maximum likelihood sequence estimation (MLSE) equalizer LSI for QPSK signals is introduced. It also supports two-branch diversity combining. Three new techniques are used to realize this fast equalizer LSI: the quantized variable-gain least mean squares (VLMS) algorithm, which has small processing delay with fast convergence characteristics; a simple complex-valued multiplication scheme based on inverting the sign and switching the in-phase and quadrature-phase components; and a parallel structure to minimize the processing delay of path memory. The chip, containing 75 kgates, is manufactured using the 0.45-μm-CMOS gate array process. The supply voltage is 3.3 V. This LSI offers higher processing speed than any other conventional equalizer chip for mobile radio communications     

Cumulant-based inverse filter criteria for MIMO blinddeconvolution: properties, algorithms, and application to DS/CDMAsystems in multipath

Tugnait (1995) and Chi and Chen proposed multi-input multi-output inverse filter criteria (MIMO-IFC) using higher order statistics for blind deconvolution of MIMO linear time-invariant systems. This paper proposes three properties on the performance of the MIMO linear equalizer associated with MIMO-IFC for any signal-to-noise ratio, including (P1) perfect phase equalization property, (P2) a relation to MIMO minimum mean square error (MIMO-MMSE) equalizer, and (P3) a connection with the one obtained by MIMO super-exponential algorithm (MIMO-SEA) that usually converges fast but does not guarantee convergence for finite data. Based on (P2), a fast algorithm for computing the theoretically optimum MIMO equalizer is proposed. Moreover, based on (P3), a fast MIMO-IFC based algorithm with performance similar to that of the MIMO-SEA and with guaranteed convergence is proposed as well as its application to suppression of multiple access interference and intersymbol interference (ISI) for multiuser asynchronous DS/CDMA systems in multipath. Finally, some simulation results are presented to support the analytic results and the proposed algorithms     

多径衰落信道MPSK信号超指数迭代盲解调算法

为了提高多径衰落信道下的盲解调性能,提出了一种结构简单的MPSK信号盲解调算法。首先利用超指数迭代分数间隔盲均衡器实现联合定时同步与均衡,然后对均衡器输出信号进行非线性变换实现载波频偏的估计,最后利用二阶数字判决锁相环跟踪相位变化纠正剩余频偏和相偏。仿真结果表明,在多径衰落信道条件下,与现有算法相比,基于超指数迭代分数间隔盲均衡器的盲解调算法实现简单,误码率低,而且具有收敛速度快、性能稳定等优点。     

Application of Fast Kalman Estimation to Adaptive Equalization

Very rapid initial convergence of the equalizer tap coefficients is a requirement of many data communication systems which employ adaptive equalizers to minimize intersymbol interference. As shown in recent papers by Godard, and by Gitlin and Magee, a recursive least squares estimation algorithm, which is a special case of the Kalman estimation algorithm, is applicable to the estimation of the optimal (minimum MSE) set of tap coefficients. It was furthermore shown to yield much faster equalizer convergence than that achieved by the simple estimated gradient algorithm, especially for severely distorted channels. We show how certain "fast recursive estimation" techniques, originally introduced by Morf and Ljung, can be adapted to the equalizer adjustment problem, resulting in the same fast convergence as the conventional Kalman implementation, but with far fewer operations per iteration (proportional to the number of equalizer taps, rather than the square of the number of equalizer taps). These fast algorithms, applicable to both linear and decision feedback equalizers, exploit a certain shift-invariance property of successive equalizer contents. The rapid convergence properties of the "fast Kalman" adaptation algorithm are confirmed by simulation.     

Adaptive minimum bit-error rate equalization for binary signaling

We consider the design and adaptation of a linear equalizer with a finite number of coefficients in the context of a classical linear intersymbol-interference channel with Gaussian noise and a memoryless decision device. If the number of equalizer coefficients is sufficient, the popular minimum mean-squared-error (MMSE) linear equalizer closely approximates the optimal linear equalizer that directly minimizes bit-error rate (BER). However, when the number of equalizer coefficients is insufficient to approximate the channel inverse, the minimum-BER equalizer can outperform the MMSE equalizer by as much as 16 dB in certain cases. We propose a simple stochastic adaptive algorithm for realizing the minimum-BER equalizer. Compared to the least-mean-square algorithm, the proposed algorithm can provide a substantial reduction in BER with no increase in complexity     

Adaptive maximum-likelihood sequence estimation by means ofcombined equalization and decoding in fading environments

This paper proposes an adaptive maximum-likelihood sequence estimation (MLSE) by means of combined equalization and decoding, i.e., adaptive combined MLSE, which employs separate channel estimation for respective states in the Viterbi algorithm. First, an approximate metric including channel estimation is derived analytically for this proposed adaptive combined MLSE. Secondly, procedures to accomplish blind equalization are investigated for the proposed MLSE. Finally, its excellent BER performance on fast time-varying fading channels is confirmed by computer simulation, when the proposed MLSE operates as a blind equalizer     

光正交频分复用系统时域均衡算法研究

提出了时域DD-LMS均衡法在O-OFDM系统中的应用。理论分析了DD-LMS均衡原理,实验验证了在直接检测O-OFDM系统中用DD-LMS均衡法比用LS均衡法的系统的平均功率代价降低了1dB;在相同接收功率下,使用DD-LMS均衡方法的系统误码率比使用LS均衡系统误码率降低很多,相应信号星座图也较收敛。