Signal Processing by Generalized Receiver in?DS-CDMA Wireless Communication Systems with?Frequency-Selective Channels

The generalized receiver (GR) based on a generalized approach to signal processing (GASP) in noise is investigated in a direct-sequence code-division multiple access (DS-CDMA) wireless communication system with frequency-selective channels. We consider four avenues: linear equalization with finite impulse response (FIR) beamforming filters; channel estimation and spatially correlation; optimal combining; and partial cancellation. We investigate the GR with simple linear equalization and FIR beamforming filters. Numerical results and simulation show that the GR with FIR beamforming filters surpasses in performance the optimum infinite impulse response beamforming filters with conventional receivers, and can closely approach the performance of GR with infinite impulse response beamforming filters. Channel estimation errors are taken into consideration so that DS-CDMA wireless communication system performance will not be degraded under practical channel estimation. GR takes an estimation error of a maximum likelihood (ML) multiple-input multiple-output (MIMO) channel estimation and GR spatially correlation into account in computation of minimum mean square error (MMSE) and log-likelihood ratio (LLR) of each coded bit. The symbol error rate (SER) performance of DS-CDMA employing GR with a quadrature sub-branch hybrid selection/maximal-ratio combining (HS/MRC) scheme for 1-D modulations in Rayleigh fading is obtained and compared with that of conventional HS/MRC receivers. Procedure of selecting a partial cancelation factor (PCF) for the first stage of a hard-decision partial parallel interference cancellation (PPIC) of the GR employed in DS-CDMA wireless communication system is proposed. A range of optimal PCFs is derived based on the Price’s theorem. Computer simulation results show superiority in bit error rate (BER) performance that is very close to that potentially achieved and surpasses the BER performance of the real PCF for DS-CDMA systems discussed in literature.     

Genetic Grey Wolf Optimizer Based Channel Estimation in Wireless Communication System

Various methods are available for channel estimation in the orthogonal frequency division multiplexing and orthogonal frequency and code division multiplexing (OFCDM) based wireless communication schemes. Along with this, the most utilized techniques are namely the minimum mean square error (MMSE) and least square (LS). The process of LS channel estimation method is simple but it occupies a very high mean square error. On the other hand, the performance of MMSE is better than LS in terms of SNR, though it shows high computational complexity. Compared to MMSE and LS based techniques, the combination of MMSE and LS techniques using evolutionary programming reduces the error significantly to receive exact signal. In this study, we propose a hybrid method namely GGWO that includes grey wolf optimization (GWO) and genetic algorithms (GA) for estimate the channel in MIMO–OFCDM schemes. At first, the best channel is estimated using GWO and afterwards, the MMSE and LS are hybridized through GA for calculating the best channel to decrease error. Overall, the GWO and GA contribute in fine tuning the obtained channel scheme so that the channel model is derived further to correlate with the ideal scheme. Our results demonstrate that the proposed scheme is superior to conventional MMSE and LS in terms of BER and SNR.     

Effects of channel estimation error on array processing based QO-STBC coded OFDM systems

This letter addresses the issues on performance degradation caused by channel estimation error of quasiorthogonal space-time block (QO-STBC) coded OFDM systems employing array processing decoder. The least square (LS) channel estimator is employed to obtain the required channel state information (CSI). Taking mean square error (MSE) as a performance metric, we evaluate the performance of the LS estimator in MIMO-OFDM systems over frequency selective channels, and its impact on symbol error rate (SER) using both analysis and simulations.     

Robust and Improved Channel Estimation Algorithm for MIMO-OFDM Systems

Multiple-input multiple-output (MIMO) system using orthogonal frequency division multiplexing (OFDM) technique has become a promising method for reliable high data-rate wireless transmission system in which the channel is dispersive in both time and frequency domains. Due to multiple cochannel interferences in a MIMO system, the accuracy of channel estimation is a vital factor for proper receiver design in order to realize the full potential performance of the MIMO-OFDM system. A robust and improved channel estimation algorithm is proposed in this paper for MIMO-OFDM systems based on the least squares (LS) algorithm. The proposed algorithm, called improved LS (ILS), employs the noise correlation in order to reduce the variance of the LS estimation error by estimating and suppressing the noise in signal subspace. The performance of the ILS channel estimation algorithm is robust to the number of antennas in transmit and receive sides. The new algorithm attains a significant improvement in performance in comparison with that of the regular LS estimator. Also, with respect to mean square error criterion and without using channel statistics, the ILS algorithm achieves a performance very close to that of the minimum mean square error (MMSE) estimator in terms of the parameters used in practical MIMO-OFDM systems. A modification of the ILS algorithm, called modified ILS (MILS), is proposed based on using the second order statistical parameters of channel. Analytically, it is shown that the MILS estimator achieves the exact performance of the MMSE estimator. Due to no specific data sequences being required to perform the estimation, in addition to the training mode, the proposed channel estimation algorithms can also be extended and used in the tracking mode with decision-aided method.     

Training sequence based channel estimation for indoor visible light communication system

Channel estimation is a key technology in indoor wireless visible light communications(VLCs).Using the training sequence(TS),this paper investigates the channel estimation in indoor wireless visible light communications.Based on the propagation and signal modulation characteristics of visible light,a link model for the indoor wireless visible light communications is established.Using the model,three channel estimation methods,i.e.,the correlation method,the least square(LS) method and the minimum mean square error(MMSE) method,are proposed.Moreover,the performances of the proposed three methods are evaluated by computer simulation.The results show that the performance of the correlation method is the worst,the LS method is suitable for higher signal to noise ratio(SNR),and the MMSE method obtains the best performance at the expense of highest complexity.     

Performance of Adaptive Modulation Scheme for Adaptive Minimum Symbol Error Rate Beamforming Receiver

This paper considers adaptive beamforming receiver that support multiple users, each having one transmit antenna. In certain circumstances, symbol error rate (SER) performance of the beamforming receiver degrades severely. In order to minimize the SER, minimum symbol error rate (MSER) beamforming receiver is utilized. Then, we propose an adaptive modulation scheme for the receiver to maintain the average SER below the target SER while maximizing the average throughput. The scheme uses the information on the direction of arrival and the average signal-to-noise ratio to decide the appropriate modulation mode. For comparison, the proposed scheme is also applied to minimum mean square error (MMSE) beamforming receiver system. Simulations were carried out in the presence of single and two interferers. Simulation results show that the performance of the proposed algorithm employing MSER beamforming is superior to its MMSE counterpart, with the largest advantage of 0.21 in the outage probability.     

Error Rate Analysis of Uplink MC-CDMA Systems under Imperfect Channel Estimation

Theoretical error rate performance of wireless communication systems are usually determined assuming that the perfect channel state information (CSI) is available at the receiver. However, in actual practice, the channel gains at the receiver are obtained via using some channel estimation (CE) techniques. Due to inherent presence of noise, the CE is not perfect resulting in the performance degradation. In this paper, we evaluate the error rate performance of an uplink multicarrier code-division multiple-access (MC-CDMA) system, considering different modulation techniques, where CE is performed using pilot symbol assisted (PSA) minimum mean-square error (MMSE) CE technique. The symbol error rate (SER) analysis of an uplink MC-CDMA system using multiuser detection techniques, such as MMSE and zero forcing (ZF), is presented under imperfect CE. Simulated results for SER are also shown to confirm the accuracy of the analytically derived results.     

Multipath combining scheme in single-carrier transmission systems

In this letter, we introduce and investigate the RAKE combining receiver which is widely used in the code-division multiple access (CDMA) systems to the non-spectrum-spreading single-carrier transmission system. The initial estimate of the transmitted data is obtained by linear single-carrier equalizers, and then all the multipath signals are constructed from this initial solution and channel impulse response. By interference cancellation (IC) technique, we can acquire every multipath component in the received signal after cancelling the sum of all the other multipath signals constructed. Finally, all the components are combined together using selection combining (SC), equal gain combining (EGC) or maximal ratio combining (MRC), so that temporal diversity gain from the combined output can be obtained. Simulation results show that bit error rate (BER) performance of the new combining receiver based on zero forcing (ZF) and minimum mean square error (MMSE) equalizers can achieve the SNR gain dramatically in the SUI-5 wireless communication link.     

Fast adaptive equalization/diversity combining for time-varyingdispersive channels

We examine adaptive equalization and diversity combining methods for fast Rayleigh-fading frequency selective channels. We assume a block adaptive receiver in which the receiver coefficients are obtained from feedforward channel estimation. For the feedforward channel estimation, we propose a novel reduced dimension channel estimation procedure, where the number of unknown parameters are reduced using a priori information of the transmit shaping filter's impulse response. Fewer unknown parameters require a shorter training sequence. We obtain least-squares, maximum-likelihood, and maximum a posteriori (MAP) estimators for the reduced dimension channel estimation problem. For symbol detection, we propose the use of a matched filtered diversity combining decision feedback equalizer (DFE) instead of a straightforward diversity combining DFE. The matched filter form has lower computational complexity and provides a well-conditioned matrix inversion. To cope with fast time-varying channels, we introduce a new DFE coefficient computation algorithm which is obtained by incorporating the channel variation during the decision delay into the minimum mean square error (MMSE) criterion. We refer to this as the non-Toeplitz DFE (NT-DFE). We also show the feasibility of a suboptimal receiver which has a lower complexity than a recursive least squares adaptation, with performance close to the optimal NT-DFE     

Improved Frequency Domain Channel Estimation and Equalization for MIMO Wireless Communications

This paper introduces an improved frequency domain channel estimation method based on interpolation vectors for single carrier frequency domain equalization (SC-FDE) with the multiple-input multiple-output (MIMO) scheme. The proposed algorithm is derived by employing the least squares (LS) criterion, and a specified application for the wide sense stationary uncorrelated scattering (WSSUS) Rayleigh fading channel is presented. The channel frequency domain responses estimated at two adjacent pilot blocks are used to track the time-variant channel information, which can effectively improve the accuracy of channel estimation without significantly increasing complexity. Maximum mean square error (MMSE) frequency domain equalization based on the estimated channel is employed in the receiver to recover transmitted signals. This paper also investigates a training sequence design method for multiple transmit antennas and a noise variance estimation method. Numerical simulation results show that the proposed methods can perform very well for fading channels with long multipath delay and high Doppler spread.     

OFDM-MIMO系统中发射分集和信道估计技术研究

文章研究了OFDM-MIMO系统中的循环延迟分集（CDD）,以及基于Alamouti的空时分组编码（STBC）与空频分组编码（SFBC）,针对发射分集方案所必须知道的信道信息,对两种信道估计方法,即最小二乘（LS）＋线性插值与最小均方误差（MMSE）插值进行了研究。通过链路仿真还给出了以上分集方案在不同信道条件下的性能曲线,此外,对两种信道估计方法的误帧率做了统计。结果表明,CDD结合信道编译码可获得分集增益,有效对抗无线信道的衰落性。STBC,SFBC在不需要外码的情况下便可获得较大分集增益,且分别适用于频率选择性衰落与时间选择性衰落信道。而MMSE估计可以获得较为理想的误帧率,为分集方案提供较准确的信道参数以提高系统的可靠性。     

基于扩展卡尔曼滤波的联合迭代检测译码信道估计方法

针对高速移动场景下信道快衰落、非平稳等特性导致下行链路信道估计性能受限的问题,提出了一种适用于高速移动环境下行链路的信道估计方法.采用自回归过程对信道建模,构造自反馈的扩展卡尔曼滤波器（Extended Kalman Filter,EKF）追踪信道响应及其时域相关系数.为了解决EKF自反馈结构引起的误差传播问题,采用了迭代检测译码的接收机结构,以利用信道编码的冗余提升EKF的信道估计精度.仿真分析表明,在高速移动环境下所提方法相较于最小二乘估计和线性最小均方误差估计等传统方法提升了信道估计的均方误差和系统的误码率性能,可应用于高速列车无线通信设备的接收机基带信号处理系统.     

A new optimized least-square sparse channel estimation scheme for underwater acoustic communication

In underwater acoustic (UWA) communication, orthogonal frequency division multiplexing (OFDM) is a promising technology that is highly essential to get channel state information meant for channel estimation (CE). Nevertheless, higher complexity, slower convergence, and poor performance, which degrade the performance estimation, are the limitations of the traditional CE methodologies. Thus, by amalgamating the least square (LS)-CE algorithm along with polynomial interpolated black widow optimization (PI-BWO) model, an optimized least square sparse (OLSS) CE algorithm has been proposed to intend for a UWA-OFDM communication system. Formerly, by utilizing the 2's complement shift left turbo encoding (2CSL-TE) methodology, the input signal is encoded. After that, the modulated encoded signal is provided for inverse fast Fourier transform (IFFT) operations; subsequently, they are transferred over the UWA channel toward the receiver OFDM. By employing the OLSS methodology, the received OFDM signal's interference-free region is utilized for sparse CE at the receiver. Regarding symbol error rate (SER), bit error rate (BER), mean square error (MSE), and peak signal-to-noise ratio (PSNR), the proposed model's experiential outcome is evaluated and analogized with the other prevailing methodologies. When analogized with the conventional models, the proposed estimation methodologies achieved better performance.     

Error probability minimizing pilots for OFDM with M-PSK modulation over Rayleigh-fading channels

Orthogonal frequency division multiplexing (OFDM) with pilot symbol assisted channel estimation is a promising technique for high rate transmissions over wireless frequency-selective fading channels. In this paper, we analyze the symbol error rate (SER) performance of OFDM with M-ary phase-shift keying (M-PSK) modulation over Rayleigh-fading channels, in the presence of channel estimation errors. Both least-squares error (LSE) and minimum mean-square error (MMSE) channel estimators are considered. For prescribed power, our analysis not only yields exact SER formulas, but also quantifies the performance loss due to channel estimation errors. We also optimize the number of pilot symbols, the placement of pilot symbols, and the power allocation between pilot and information symbols, to minimize this loss, and thereby minimize SER. Simulations corroborate our SER performance analysis, and numerical results are presented to illustrate our optimal claims.     

Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals

In this paper, we study the performance of multiple-input multiple-output channel estimation methods using training sequences. We consider the popular linear least squares (LS) and minimum mean-square-error (MMSE) approaches and propose new scaled LS (SLS) and relaxed MMSE techniques which require less knowledge of the channel second-order statistics and/or have better performance than the conventional LS and MMSE channel estimators. The optimal choice of training signals is investigated for the aforementioned techniques. In the case of multiple LS channel estimates, the best linear unbiased estimation (BLUE) scheme for their linear combining is developed and studied.     

Pilot-based estimation of time-varying multipath channels forcoherent CDMA receivers

Reliable coherent wireless communication requires accurate estimation of the time-varying multipath channel. This paper addresses two issues in the context of direct-sequence code-division multiple access (CDMA) systems: (i) linear minimum-mean-squared-error (MMSE) channel estimation based on a pilot transmission and (ii) impact of channel estimation errors on coherent receiver performance. A simple characterization of the MMSE estimator in terms of a bank of filters is derived. A key channel characteristic controlling system performance is the normalized coherence time, which is approximately the number of symbols over which the channel remains strongly correlated. It is shown that the estimator performance is characterized by an effective signal-to-noise ratio (SNR)-the product of the pilot SNR and the normalized coherence time. A simple uniform averaging estimator is also proposed that is easy to implement and delivers near-optimal performance if properly designed. The receivers analyzed in this paper are based on a time-frequency RAKE structure that exploits joint multipath-Doppler diversity. It is shown that the overall receiver performance is controlled by two competing effects: shorter coherence times lead to degraded channel estimation but improved inherent receiver performance due to Doppler diversity, with opposite effects for longer coherence times. Our results demonstrate that exploiting Doppler diversity can significantly mitigate the error probability floors that plague conventional CDMA receivers under fast fading due to errors in channel estimation     

Efficient Channel Estimation for MIMO Single-Carrier Block Transmission With Dual Cyclic Timeslot Structure

We investigate channel estimation for timeslot-structured single-carrier block transmission (SCBT) over space-, time-, and frequency-selective fading multiple-input multiple-output (MIMO) channels. A MIMO-SCBT with a dual cyclic timeslot structure is presented first. Then, an optimal channel estimation in the minimal mean square error (MMSE) sense on the timeslot basis is investigated. It is shown that the optimal pilots for the timeslot-based MMSE channel estimation are related to the statistical channel state information in eigenmode. Under the assumption that the transmit correlation is unknown at the transmitter, the optimal pilots satisfy the same condition as reported for the block-based least-square (LS) channel estimation in literature, and the channel estimation can be simplified to initial block-based LS channel estimation followed by space-time postprocessing. Particularly, for spatially uncorrelated channels, the space-time postprocessing can be reduced to pathwise processing. A new design of the pilot sequences is given, which leads to an efficient implementation of the channel estimation. Later on, a more efficient implementation for the initial channel estimation is obtained by using the structure of the pilot sequences, and discrete cosine transform (DCT)-based implementation is developed for the space-time postprocessing to approximate the optimal solution with low implementation complexity. Finally, the performance of the proposed channel estimation is verified via simulations.     

Performance analysis of channel estimation in amplify‐and‐forward multi‐hop direct forwarding wireless networks

In this paper, we study the performance of a training‐based least square (LS) and linear minimum mean‐square‐error (LMMSE) channel estimation for both hop‐by‐hop and multi‐hop direct forwarding wireless sensor networks over frequency‐selective fading channels. Specifically, to investigate the properties of the channel estimation, we accomplish a theoretical analysis of MSE in terms of various link parameters. From the performance evaluation, we analytically present the effects of the number of hops on the MSE performance for channel estimations in both multi‐hop networks. Interesting observations of MSE behaviors under various conditions are discussed, and the receiver complexity and channel equalization performance are also analyzed. Finally, through the computer simulations, the analytical results and detection performance are demonstrated. Copyright © 2014 John Wiley & Sons, Ltd.     

Channel estimation in space and frequency domain for MIMO-OFDM systems

Multiple-input multiple-output (MIMO) systems can be combined with orthogonal frequency division multiplexing (OFDM) systems to improve the capacity and quality of wireless communications. In this article, a channel estimation technique in both space and frequency domain for MIMO-OFDM systems is proposed. It is shown that the proposed scheme with space-frequency pilot tones achieve optimal minimum mean square error (MMSE) channel estimation. Simulation results indicate that the proposed method achieves good performance.     

Subspace Projection-based OFDM Channel Estimation

In this paper, we investigate the benefits of pre-processing received data by projection on the performance of channel estimation for orthogonal frequency division multiplexing (OFDM) systems. Projecting data onto its signal subspace will reduce the additive noise energy in the data. Least square (LS) estimation is a low-complex algorithm for training-based OFDM systems and the lower bound on the mean-square error of it is proportional to the noise variance. So, after the received data is pre-processed (projected onto its signal subspace), LS channel estimation on the pre-processed data will increase the performance of channel estimation. This method can also work in multiple-input and multiple-output (MIMO) case. Performance analysis and simulation results show that the proposed algorithm has a considerably smaller complexity than the linear minimum mean square error estimation while having almost the same performance.