Estimated Channel Coefficients Research Articles

Coded Decision-Directed Channel Estimation for Coherent Detection in Terrestrial DMB Receivers

This paper proposes a coded decision-directed (DD) channel-estimation method with two-dimensional averaging for T-DMB receivers. Since the noncoherent differential demodulation of existing T-DMB receivers can not track channel variation, it may not work well in fast-fading channels. To overcome this problem, coherent detection based on channel estimation is introduced. The proposed method re- modulates the output of the soft-decision Viterbi decoder for coded DD channel estimation to obtain more accurate decision symbols than hard-decision symbols. To both mitigate the effects of noise and smooth incorrectly-estimated coefficients, the estimated channel coefficients are two- dimensionally averaged. The enhanced accuracy leads to the high-performance DQPSK demodulation robust to Doppler spread and noise. The proposed method may also contribute to the launch of the future T-DMB service of providing a higher data rate. Simulation results show that the proposed scheme performs much better than the conventional method, especially at high speed-moving receivers.

Noise variance estimation and optimal sequences for channel estimation in SC-FDE UWB systems

Noise variance is an important parameter for single-carrier ultra-wideband systems with frequency domain equalisation. The conventional method cannot obtain the noise variance in the case of using the estimated channel coefficients by least square (LS) criteria, whereas the LS channel estimator is a promising candidate owing to its easy implementation. To overcome this contradiction, proposed is a novel noise variance estimator using the LS channel coefficient estimates with the aid of a complex pilot sequence. Also derived is the Cramer-Rao bound on the proposed estimator. In addition, Chu's sequence is chosen as the optimal pilot sequence of LS channel estimation without increase of peak-to-average power ratio and with easy realisation. Simulations demonstrate the performance of the proposed schemes.

Multiuser Channel Estimation Using Gaussian Mixture Sigma Point Particle Filter for CDMA System

In this paper, a multiuser receiver based on a Gaussian Mixture Sigma Point Particle Filter (GMSPPF), which can be used for joint channel coefficient estimation and time delay tracking in CDMA communication systems, is introduced. The proposed algorithm has better improved estimation performance than either Extended Kalman Filter (EKF) or Particle Filter (PF). The Cramer-Rao Lower Bound (CRLB) is derived for the estimator, and the simulation result demonstrates that it is almost completely near-far resistant. For this reason, it is believed that the proposed estimator can replace well-known filters such as the EKF or PF.

SIMULASI IDENTIFIKASI KANAL LINTAS-JAMAK PADA WCDMA SECARA ADAPTIF DENGAN FILTER NLMS DAN LMS

This research was solely conducted to observe the adaptive filter ability in identification of multipath channel for Wideband Code Division Multiple Access (WCDMA). The method used was limited to a simulation of mutipath channel identification by adaptive filter, which imitated the characteristics of the channel. LMS (Least Mean Square) and NLMS (Normalized Least Mean Square) filter were observed, where as Kalman was used to compare the results. There were some variable to be varied in this simulation, namely the convergence variable, the filter length, and the SNR. Channel identification analysis is based on the estimated channel coefficients compared with channel coefficients, the convergence constant of adaptive filter, the adaptation time, the mean square errors (MSE), and the bit error rates. The results show that NLMS filter has a good performance in channel identification. LMS filter has largest mean square error in the channel identification. Kalman gives more precise results but has complex algorithm. Kalman left the least mean square error, which is 4.1e-34 at 0.16 of convergence rate. All filters have good performance on signal detection in various signal to noise ratio, especially for SNR ³ 10 dB. Bit error rate at 10 dB SNR is 3.33e-4.

Decision-directed estimation of MIMO time-varying Rayleigh fading channels

This paper presents a decision-directed (DD) maximum a posteriori probability (MAP) channel-estimation scheme for multiple-input multiple-output (MIMO) time-varying fading channels. With the estimate of the channel matrix for the current symbol interval, a zero-forcing (ZF) receiver is applied to detect the spatially multiplexed data on a symbol-by-symbol basis. Symbol decisions are then fed to the channel predictor for estimation of channel coefficients in future symbol intervals. Simulated error performance of a ZF receiver with the DD MAP and perfect channel estimates is provided and compared.

Performance of coded DS-CDMA with pilot-assisted channel estimation and linear interference suppression

We consider a direct sequence (DS-) code division multiple access (CDMA) system with orthogonally multiplexed pilot signals and minimum mean squared error (MMSE) data and channel estimation. Both flat and frequency-selective fading channels are considered. Large system analysis is used to optimize the pilot-to-data power ratio (PDR) and the code rate for a fixed bandwidth expansion. Specifically, the PDR is selected to minimize the probability of error subject to a constraint on transmitted power. When the MMSE filter estimates the channel of the desired user, but averages over the channels of the interferers (corresponding to an adaptive filter in moderate to fast fading), the optimal PDR is less than that for the matched filter (MF). That is, the MMSE filter benefits from allocating more power to the data. When the MMSE filter directly incorporates estimates of all users' channel coefficients, the optimal PDR is greater than that for the MF. System performance as a function of code rate is characterized through both probability of error and cutoff rate. The optimal code rate for the MMSE receiver is generally higher than that for the MF, and increases with load and E/sub b//N/sub 0/. In the presence of fading, and with channel estimation, the optimal code rate approaches zero for both MMSE and MF receivers, but the MMSE filter is more robust with respect to a suboptimal choice of code rate.

Multiuser receivers for CDMA systems in Rayleigh fading channels

Multiuser demodulation in relatively fast fading channels is analyzed. The optimal maximum likelihood sequence detection (MLSD) receiver is derived and a general suboptimal receiver to approximate the MLSD is proposed. The performance of the parallel interference cancellation (PIC) and decorrelating receivers is compared. The PIC receiver is demonstrated to achieve better performance in known channels than the decorrelating receiver, but it is observed to be more sensitive to the channel coefficient estimation errors than the decorrelator. At high channel loads the PIC receiver suffers from bit error rate (BER) saturation, whereas the decorrelating receiver does not. The performance of data-aided (DA) and decision-directed (DD) multiuser channel estimation is also compared. DA channel estimation is shown to be more robust than DD channel estimation, which may suffer from BER saturation caused by hangups at high signal-to-noise ratios (SNRs). The impact of channel estimation filter impulse response on the BER is studied by comparing optimal and suboptimal channel estimation filters. The implementation complexity of the decorrelating and PIC receivers is compared in terms of required floating point operations and clock cycles in a practical communication scenario. It is observed that the PIC receiver is only moderately more complex to implement than the conventional matched filter bank receiver, whereas the decorrelating receiver is significantly more complex.

Optimally regularized channel tracking techniques for sequence estimation based on cross-validated subspace signal processing

New methods for maximum-likelihood sequence estimation based on the Viterbi algorithm (VA) are presented. In the proposed scheme, the channel estimator and the Viterbi processor operate concurrently. At any given time-step, the sequence provided to the channel estimator comes from the survivor with the best metric value. These already known modifications of the traditional decision-directed VA cause large variance in the estimated channel coefficients. In fact, sequences with a high error rate may be used to perform estimation, and also the adjustment term of the channel tracking algorithm may exhibit abrupt changes caused by a "survivor swap", (that is by the event in which a different survivor has the best metric at step n, with respect to the step n-1). The proposed regularization procedure forces the channel vector to lie in the appropriate a priori known subspace: while the variance decreases, a certain amount of bias is introduced. The variance-bias tradeoff is then automatically adjusted by means of a cross-validation "shrinkage" estimator, which is at the same time optimal in a "small sample" predictive sum of squares sense and asymptotically model mean squared-error optimal. The method is shown by means of hardware experiments on a wide-band base station to be extremely more effective than per survivor processing, minimum survivor processing, and traditional decision directed approaches.

A two-sensor array blind beamformer for direct sequence spread spectrum communications

We present an efficient blind beamformer dedicated to the problem of interference mitigation in direct sequence spread spectrum (DSSS) communication systems using a two-sensor array. A closed-form solution for the blind identification of the communication channel is derived by exploiting the temporal properties of the desired signal and the interference. The optimal beamformer is derived from the maximization of the signal-to-interference and noise ratio (SINR) at the output of the receiver in terms of the blindly estimated channel coefficients. Three structures of the DSSS receiver are presented. One structure consists of the blind beamformer followed by the spread spectrum demodulator. The other two structures consist of the spread spectrum demodulator followed by the blind beamformer. The performance of these structures is discussed in terms of the achieved SINR and the computational cost. Simulation results are provided to illustrate the effectiveness of the proposed blind beamformers in interference excision.

Parallel Interference Cancellation in Multiuser CDMA ChannelEstimation

Parallel interference cancellation (PIC) based channel parameter estimators for frequency selective fading channels are proposed for the uplink in code-division multiple-access (CDMA) mobile communication systems. The performance of PIC based algorithms depends heavily on the quality of the multiple-access interference estimates, which can be improved by using adaptive channel estimation filters. The performance of two adaptive complex channel coefficient estimation filters has been verified in a fading channel by computer simulations. According to the results, the PIC based adaptive channel estimators outperform clearly conventional, successive interference cancellation, and decorrelation based adaptive channel estimators. The PIC method is also used in delay tracking. By using the principles of sample-correlate-choose-largest (SCCL) delay trackers, a robust algorithm for multiuser delay tracking in fading channels is obtained.

Influence of channel estimation on the performance of a coherent DS-CDMA system

Direct-sequence code division multiple access (DS-CDMA) has become one of the favourite candidates for future mobile radio communication systems. The uplink of a coherent DS-CDMA system is investigated. The channel estimation is carried out via a control channel, which is transmitted simultaneously to the data channel. This concept is very closely related to the CODIT-CDMA concept. In the channel estimation, an averaging process is involved, which for long averaging periods, suppresses the noise influence efficiently. On the other hand, long averaging intervals lead to systematic errors in the estimated channel coefficients due to the time-variant behavior of the channel. Taking into account these effects, a closed-form analysis of the data-channel symbol-error rate (SER) is presented. This analysis allows the prediction of the system's performance and helps to optimize the parameter settings for channel estimation.

Blind equalization of IIR channels using hidden Markov models and extended least squares

In this paper, we present a blind equalization algorithm for noisy IIR channels when the channel input is a finite state Markov chain. The algorithm yields estimates of the IIR channel coefficients, channel noise variance, transition probabilities, and state of the Markov chain. Unlike the optimal maximum likelihood estimator which is computationally infeasible since the computing cost increases exponentially with data length, our algorithm is computationally inexpensive. Our algorithm is based on combining a recursive hidden Markov model (HMM) estimator with a relaxed SPR (strictly positive real) extended least squares (ELS) scheme. In simulation studies we show that the algorithm yields satisfactory estimates even in low SNR. We also compare the performance of our scheme with a truncated FIR scheme and the constant modulus algorithm (CMA) which is currently a popular algorithm in blind equalization.