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.     

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     

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.     

平均自适应滤波的信道均衡算法研究

近年来数字传输系统的信道均衡侧重于训练时间的缩短和跟踪速度的加快,需要研究快速收敛的自适应算法。从这点考虑递归最小二乘(RLS)均衡器是最佳的选择,但RLS算法的运算非常复杂而且存在稳定性问题,因而有必要研究一种能够代替传统RLS的算法。在本文中介绍一种基于平均自适应滤波(AFA)算法的均衡器,其主要优点是与RLS算法相当的快速收敛速度,同时运算复杂度较低。     

Adaptive MIMO decision feedback equalization for receivers with time-varying channels

In an attempt to reduce the computational complexity of vertical Bell Labs layered space time (V-BLAST) processing with time-varying channels, an efficient adaptive receiver is developed based on the generalized decision feedback equalizer (GDFE) architecture. The proposed receiver updates the filter weight vectors and detection order using a recursive least squares (RLS)-based time- and order-update algorithm. The convergence of the algorithm is examined by analysis and simulation, and it is shown that the proposed adaptive technique is considerably simpler to implement than a V-BLAST processor with channel tracking, yet the performances are almost comparable.     

Doubly Iterative Equalization of Continuous-Phase Modulation

In this paper, a doubly iterative receiver is proposed for joint turbo equalization, demodulation, and decoding of coded binary continuous-phase modulation (CPM) in multipath fading channels. The proposed receiver consists of three soft-input soft-output (SISO) blocks: a front-end soft-information-aided minimum mean square error (MMSE) equalizer followed by a CPM demodulator and a back-end channel decoder. The MMSE equalizer, combined with an a priori soft-interference canceler (SIC) and an a posteriori probability mapper, forms a SISO processor suitable for iterative processing that considers discrete-time CPM symbols which belong to a finite alphabet. The SISO CPM demodulator and the SISO channel decoder are both implemented by the a posteriori probability algorithm. The proposed doubly iterative receiver has a central demodulator coupled with both the front-end equalizer and the back-end channel decoder. A few back-end demodulation/decoding iterations are performed for each equalization iteration so as to improve the a priori information for the equalizer. As presented in the extrinsic information transfer (EXIT) chart analysis and simulation results for different multipath fading channels, this provides not only faster convergence to low bit error rates, but also lower computational complexity.     

Blind adaptive code-constrained constant modulus algorithms for CDMA interference suppression in multipath channels

A code-constrained constant modulus (CCM) design criterion for linear receivers is investigated for direct sequence code division multiple access (DS-CDMA) in multipath channels based on constrained optimization techniques. A computationally efficient recursive least squares (RLS) type algorithm for jointly estimating the parameters of the channel and the receiver is developed in order to suppress multiaccess (MAI) and inter-symbol interference (ISI). An analysis of the method examines its convergence properties and simulations under nonstationary environments show that the novel algorithms outperform existent techniques.     

Efficient bad equalizer in two-ray multipath fading channels

An efficient bidirectional arbitrated decision feedback (BAD) equalizer is presented in single-carrier block transmission system in the Two-Ray multipath fading channels, where the output from the bidirectional equalizers are combined together directly using maximal ratio combining (MRC) rule to improve the signal-to-noise ratio (SNR) before demodulation. The computational complexity of the BAD equalizer presented is linear with the channel length, which is the same as conventional decision feedback equalizer (DFE) and is significantly lower than that of conventional BAD equalizer as well as the maximum likelihood (ML) algorithm. While the performance of the new scheme depends on the specific channel characteristics, it is shown by simulation results that the performance of the new BAD can surpass the one of DFE dramatically in the minimum or non-minimum phase Two-Ray multipath fading channels.     

Adaptive power techniques for blind channel estimation in CDMA systems

The problem of blind adaptive channel estimation in code-division multiple access (CDMA) systems is considered. Motivated by the iterative power method, which is used in numerical analysis for estimating singular values and singular vectors, we develop recursive least squares (RLS) and least mean squares (LMS) subspace-based adaptive algorithms in order to identify the impulse response of the multipath channel. The schemes proposed in this paper use only the spreading code of the user of interest and the received data and are therefore blind. Both versions (RLS and LMS) exhibit rapid convergence combined with low computational complexity. With the help of simulations, we demonstrate the improved performance of our methods as compared with the already-existing techniques in the literature.     

A robust adaptive DFE receiver for DS-CDMA systems under multipathfading channels

This paper presents a novel receiver design from signal processing viewpoint for direct-sequence code-division multiple access (DS-CDMA) systems under multipath fading channels. A robust adaptive decision-feedback equalizer (DFE) is developed by using optimal filtering technique via minimizing the mean-square error (MSE). The multipath fading channels are modeled as tapped-delay-line filters, and the tap coefficients are described as Rayleigh distributions in order to imitate the frequency-selective fading channel. Then, a robust Kalman filtering algorithm is used to estimate the channel responses for the adaptation of the proposed DFE receiver under the situation of partially known channel statistics. The feedforward and feedback filters are designed by using not only the estimated channel responses but the uncertainties and error covariance of channel estimation as well. As shown in the computer simulations, the proposed adaptive DFE receiver is robust against the estimation errors and modeling dynamics of the channels. Hence, it is very suitable for receiver design in data transmissions through multipath fading channels encountered in most wireless communication systems     

A new adaptive equalizer with channel estimator for mobile radiocommunication

For unknown mobile radio channels with severe intersymbol interference (ISI), a maximum likelihood sequence estimator, such as a decision feedback equalizer (DFE) having both feedforward and feedback filters, needs to handle both precursors and postcursors. Consequently, such an equalizer is too complex to be practical. This paper presents a new reduced-state, soft decision feedback Viterbi equalizer (RSSDFVE) with a channel estimator and predictor. The RSSDFVE uses maximum likelihood sequence estimation (MLSE) to handle the precursors and truncates the overall postcursors with the soft decision of the MLSE to reduce the implementation complexity. A multiray fading channel model with a Doppler frequency shift is used in the simulation. For fast convergence, a channel estimator with fast start-up is proposed. The channel estimator obtains the sampled channel impulse response (CIR) from the training sequence and updates the RSSDFVE during the bursts in order to track changes of the fading channel. Simulation results show the RSSDFVE has nearly the same performance as the MLSE for time-invariant multipath fading channels and better performance than the DFE for time-variant multipath fading channels with less implementation complexity than the MLSE. The fast start-up (FS) channel estimator gives faster convergence than a Kalman channel estimator. The proposed RSSDFVE retains the MLSE structure to obtain good performance and only uses soft decisions to subtract the postcursor interference. It provides the best tradeoff between complexity and performance of any Viterbi equalizers     

Joint MAP equalization and channel estimation forfrequency-selective and frequency-flat fast-fading channels

This paper presents a new fractionally-spaced maximum a posteriori (MAP) equalizer for data transmission over frequency-selective fading channels. The technique is applicable to any standard modulation technique. The MAP equalizer uses an expanded hypothesis trellis for the purpose of joint channel estimation and equalization. The fading channel is estimated by coupling minimum mean square error techniques with the (fixed size) expanded trellis. The new MAP equalizer is also presented in an iterative (turbo) receiver structure. Both uncoded and conventionally coded systems (including iterative processing) are studied. Even on frequency-flat fading channels, the proposed receiver outperforms conventional techniques. Simulations demonstrate the performance of the proposed equalizer     

Semi-blind maximum-likelihood joint channel/data estimation for correlated channels in multiuser MIMO networks

The aim of this paper is to investigate receiver techniques for maximum likelihood (ML) joint channel/data estimation in flat fading multiple-input multiple-output (MIMO) channels, that are both (i) data efficient and (ii) computationally attractive. The performance of iterative least squares (LS) for channel estimation combined with sphere decoding (SD) for data detection is examined for block fading channels, demonstrating the data efficiency provided by the semi-blind approach. The case of continuous fading channels is addressed with the aid of recursive least squares (RLS). The observed relative robustness of the ML solution to channel variations is exploited in deriving a block QR-based RLS-SD scheme, which allows significant complexity savings with little or no performance loss. The effects on the algorithms’ performance of the existence of spatially correlated fading and line-of-sight paths are also studied. For the multi-user MIMO scenario, the gains from exploiting temporal/spatial interference color are assessed. The optimal training sequence for ML channel estimation in the presence of co-channel interference (CCI) is also derived and shown to result in better channel estimation/faster convergence. The reported simulation results demonstrate the effectiveness, in terms of both data efficiency and performance gain, of the investigated schemes under realistic fading conditions.     

An adaptive correlator receiver for direct-sequence spread-spectrumcommunication

This paper presents a novel receiver for direct sequence spread-spectrum signals over channels containing interference and multipath. The receiver employs an adaptive correlator that jointly detects the transmitted data, removes interference, and compensates for multipath. The optimum correlation vector is derived by determining the Wiener vector that minimizes the mean squared error (MSE) between the transmitted data bit and the correlator output. For an additive white Gaussian noise (AWGN) channel, the optimal correlation vector is the spreading sequence used by the transmitter. For interference and multipath channels, the optimal correlation vector will suppress the interference and combine the multipath while optimizing the detection of the transmitted data bit. The paper presents analytical and simulation results which illustrate the bit-error rate (BER) performance of the receiver in multipath and narrowband interference. Additionally, simulation results are presented illustrating the convergence performance of the receiver when the tap weights are adjusted using either the least mean square (LMS) or recursive least squares (RLS) adaptive algorithms     

Adaptive MLSE equalizers with parametric tracking for multipathfast-fading channels

We use the parametric channel identification algorithm proposed by Chen and Paulraj (see Proc. IEEE Vehicular Technology Conf., p.710-14, 1997) and by Chen, Kim and Liang (see IEEE Trans. Veh. Technol., p.1923-35, 1999) to adaptively track the fast-fading channels for the multichannel maximum likelihood sequence estimation (MLSE) equalizer using multiple antennas. Several commonly-used channel tracking schemes, decision-directed recursive least square (DD/RLS), per-survivor processing recursive least square (PSP/RLS) and other reduced-complexity MLSE algorithms are considered. An analytic lower bound for the multichannel MLSE equalizer with no channel mismatch in the time-varying specular multipath Rayleigh-fading channels is derived. Simulation results that illustrate the performance of the proposed algorithms working with various channel tracking schemes are presented, and then these results are compared with the analytic bit error rate (BER) lower bound and with the conventional MLSE equalizers directly tracking the finite impulse response (FIR) channel tap coefficients. We found that the proposed algorithm always performs better than the conventional adaptive MLSE algorithm, no matter what channel tracking scheme is used. However, which is the best tracking scheme to use depends on the scenario of the system     

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.     

Analysis of LMS-adaptive MLSE equalization on multipath fadingchannels

We consider a practical maximum-likelihood sequence estimation (MLSE) equalizer on multipath fading channels in conjunction with an adaptive channel estimator consisting of a least mean square (LMS) estimator and a linear channel predictor, instead of assuming perfect channel estimates. A new LMS estimator model is proposed which can accurately characterize the statistical behavior of the LMS estimator over multipath fading channels. Based on this model, a new upper-bound on block error rate is derived under the consideration of imperfect channel estimates. Computer simulations verify that our analytical results can correctly predict the real system performance and are applicable over a wide range of the step size parameter of the LMS estimator     

Improved Soft Iterative Channel Estimation for Turbo Equalization of Time Varying Frequency Selective Channels

In this paper, we present computationally efficient iterative channel estimation algorithms for Turbo equalizer-based communication receiver. Least Mean Square (LMS) and Recursive least Square (RLS) algorithms have been widely used for updating of various filters used in communication systems. However, LMS algorithm, though very simple, suffers from a relatively slow and data dependent convergence behaviour; while RLS algorithm, with its fast convergence rate, finds little application in practical systems due to its computational complexity. Variants of LMS algorithm, Variable Step Size Normalized LMS (VSSNLMS) and Multiple Variable Step Size Normalized LMS algorithms, are employed through simulation for updating of channel estimates for turbo equalization in this paper. Results based on the combination of turbo equalizer with convolutional code as well as with turbo codes alongside with iterative channel estimation algorithms are presented. The simulation results for different normalized fade rates show how the proposed channel estimation based-algorithms outperformed the LMS algorithm and performed closely to the well known Recursive least square (RLS)-based channel estimation algorithm.     

Complex noncoherent receivers for GMSK signals

Noncoherent demodulators are very attractive for high performance radio LAN (HIPERLAN) systems because of their low implementation costs and their inherent robustness against frequency and carrier phase offsets. However, when the channel is time dispersive, the nonlinear intersymbol interference (ISI) introduced by these demodulators precludes the use of conventional linear equalization strategies. We present an alternative noncoherent receiver structure followed by a nonlinear equalizer, which includes a RAM and a Viterbi detector, capable of equalizing nonlinear multipath fading channels. In addition, we also present a new algorithm specifically for noncoherent demodulators, which allows estimation of all useful signal values at the input of the equalizer to be stored in the RAM. By means of computer simulations, we report the performance and computational complexity tradeoffs of the receiver/equalizer structure, including antenna diversity. We show that demodulators which consist of a complex receiver and a Viterbi detector are much more robust against multipath fading channels than traditional real noncoherent demodulators. The results suggest that in a typical HIPERLAN scenario, where the channel delay spread is less than 50 ns and a reliable line of sight component exists, it is feasible to combat multipath effects using noncoherent demodulation     

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.