多载波DS—CDMA宽带分组传输中的重复路径搜索和信道估计方法的特性

本文提出一种在无线传输带宽可达100MHz的超宽带多载波DS-CDMA分组传输中采用高精度的重复路径搜索和信道估计法的方案。本方案的特点是，在只用导频码进行最初的路径搜索和信道估计及瑞克合成和纠错译码后，再将这个经译码后的判定反馈数据码加在导频码上反复进行路径搜索和信道估计。计算机模拟结果表明，采用全部64个导频码和重复数为1以及等平均功率为24条路径瑞利衰落的信道模型，子波数为1，4，8，和16时，所需平均接收Eb/No满足平均分组误码率等于10∧-2的条件，比不进行重复路径搜索和信道估计时，分别改善了1.2，1.3,2.0和1.2dB。

New subspace algorithm for blind channel estimation in OFDM systems

In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because the transmission equation of OFDM systems does not exactly have the desired structure to directly derive a subspace algorithm,the algorithm first divides the OFDM signals into three parts,then,by exploiting the redundancy introduced by the cyclic prefix (CP) in OFDM signals,a new equation with Toeplitz channel matrix is derived.Based on the equation,a new blind subspace algorithm is developed.Toeplitz structure eases the derivation of the subspace algorithm and practical computation.Moreover the algorithm does not change the existing OFDM system,is robust to channel order overdetermination,and the channel zero locations.The performances are demonstrated by simulation results.

Compressed sensing based channel estimation using group random unit pilot for OFDM systems in fast moving environment~

A compressed sensing （CS） based channel estimation algorithm is proposed in the fast moving environment. A sparse basis expansion channel model in both time and frequency domain is given.Pilots are placed according to a novel random unit pilot matrix （RUPM） to measure the delay- Doppler sparse channel. The sparse channels are recovered by an extension group orthogonal matching pursuit （GOMP） algorithm, enjoying the diversity gain from multi-symbol processing. The relatively nonzero channel coefficients are estimated from a very limited number of pilots at a sampling rate significantly below the Nyquist rate. The simulation results show that the new channel estimator can provide a considerable performance improvement for the fast fading channels. Three significant reductions are achieved in the required number of pilots, memory requirements and computational complexity.

Semi-blind Channel Estimator for OFDM-STC

Channel state information of OFDM-STC system is required for maximum likelihood decoding.A subspace-based semi-blind method was proposed for estimating the channels of OFDM-STC systems.The channels are first estimated blindly up to an ambiguity parameter utilizing the nature structure of STC,irrespective of the underlying signal constellations.Furthermore,a method was proposed to resolve the ambiguity by using a few pilot symbols.The simulation results show the proposed semi-blind estimator can achieve higher spectral efficiency and provide improved estimation performance compared to the non-blind estimator.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

ADAPTIVE BLIND ESTIMATION ALGORITHM FOR OFDM-MIMO RADIO SYSTEMS OVER MULTIPATH CHANNELS

This paper investigates the blind algorithm for channel estimation of Orthogonal Frequency Division Multiplexing-Multiple Input Multiple Output (OFDM-MIMO) wireless communication system using the subspace decomposition of the channel received complex baseband signals and proposes a new two-stage blind algorithm. Exploited the second-order cyclostationarity inherent in OFDM with cyclic prefix and the characteristics of the phased antenna, the practical HIPERLAN/2 standard based OFDM-MIMO simulator is established with the sufficient consideration of statistical correlations between the multiple antenna channels under wireless wideband multipath fading environment, and a new two-stage blind algorithm is formulated using rank reduced subspace channel matrix approximation and adaptive Constant Modulus (CM)criterion. Simulation results confirm the theoretical analysis and illustrate that the proposed algorithm is capable of tracking matrix channel variations with fast convergence rate and improving acceptable overall system performance over various common wireless and mobile communication links.

Maximum likelihood channel estimation algorithm combines logarithm likelihood ratio decoding in the coal mine application

The environment of the wireless communication system in the coal mine has unique characteristics： great noise, strong multiple path interference, and the wireless communication of orthogonal frequency division multiplexing （OFDM） in underground coal mine is sensitive to the frequency selection of multiple path fading channel, whose decoding is separated from the traditional channel estimation algorithm. In order to increase its accuracy and reliability, a new iterating channel estimation algorithm combining the logarithm likelihood ratio （LLR） decode iterate based on the maximum likelihood estimation （ML） is proposed in this paper, which estimates iteration channel in combination with LLR decode. Without estimating the channel noise power, it exchanges the information between the ML channel estimation and the LLR decode using the feedback information of LLR decode. The decoding speed is very quick, and the satisfied result will be obtained by iterating in some time. The simulation results of the shortwave broadband channel in the coal mine show that the error rate of the system is basically convergent after the iteration in two times.

Semi-Blind Pilot-Aided Channel Estimation in Uplink Cloud Radio Access Networks

In this paper, a quasi-Newton method fbr semi-blind estimation is derived for channel estimation in uplink cloud radio access networks （C-RANs）. Different from traditional pilot-aided estimation, semiblind estimation utilizes the unknown data symbols in addition to the known pilot symbols to estimate the channel. An initial channel state information （CSI） obtained by least-squared （LS） estimation is needed in semi-blind estimation. BFGS （Brayben, Fletcher, Goldfarb and Shanno） algorithm, which employs data as well as pilot symbols, estimates the CSI though solving the problem provided by maximum-likelihood （ML） principle. In addition, mean-square-error （MSE） used to evaluate the estimation performance can be further minimized with an optimal pilot design. Simulation results show that the semi-blind estimation achieves a significant improvement in terms of MSE performance over the conventional LS estimation by utilizing data symbols instead of increasing the number of pilot symbols, which demonstrates the estimation accuracy and spectral efficiency are both improved by semiblind estimation for C-RANs.

Simplified p-norm-like Constraint LMS Algorithm for Efficient Estimation of Underwater Acoustic Channels

Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation of sparsity contained in underwater acoustic channels provides a potential solution to improve the performance of underwater acoustic channel estimation. Compared with the classic 10 and 11 norm constraint LMS algorithms, the p-norm-like （Ip） constraint LMS algorithm proposed in our previous investigation exhibits better sparsity exploitation performance at the presence of channel variations, as it enables the adaptability to the sparseness by tuning of p parameter. However, the decimal exponential calculation associated with the p-norm-like constraint LMS algorithm poses considerable limitations in practical application. In this paper, a simplified variant of the p-norm-like constraint LMS was proposed with the employment of Newton iteration m to approximate the decimal exponential calculation. Num simulations and the experimental results obtained in physical shallow water channels demonstrate the effectiveness of the proposed method compared to traditional norm constraint LMS algorithms.

Research on underwater acoustic channel estimation and temperature factors based on FBMC

The complexity of underwater environment poses a challenge to underwater acoustic communication.In marine environment,different temperatures,depths and salinities would affect the performance of acoustic communication.The analysis of the underwater acoustic channel under the influence of temperature factors provides a reference for further study of the underwater acoustic channel estimation problem based on filter bank multi-carrier(FBMC).The FBMC based offset quadrature amplitude modulation(OQAM)technology(FBMC/OQAM)was introduced into the underwater acoustic communication.Based on FBMC,the underwater acoustic channel estimation technology was studied.By changing the pilot structure to adapt to the complex and variable underwater acoustic channel,the iterative method was used to obtain the channel information with higher accuracy and further improve the performance of channel estimation.Theoretical analysis and simulation results show that iterative channel estimation algorithm based on the new interference approximation method(IAM)pilot proposed in this paper has better performance in underwater acoustic channel.

BLIND CHANNEL ESTIMATION IN DELAY DIVERSITY FOR FREQUENCY SELECTIVE CHANNELS

Delay diversity is an effective transmit diversity technique to combat adverse effects of fading. Thus far, previous work in delay diversity assumed that perfect estimates of current channel fading conditions are available at the receiver and training symbols are required to estimate the channel from the transmitter to the receiver. However, increasing the number of the antennas increases the required training interval and reduces the available time with in whichdata may be transmitted. Learning the channel coefficients becomes increasingly difficult for the frequency selective channels. In this paper, with the subspace method and the delay character of delay diversity, a channel estimation method is proposed, which does not use training symbols. It addresses the transmit diversity for a frequency selective channel from a single carrier perspective in the form of a simple equivalent flat fading model. Monte Carlo simulations give the performance of channel estimation and the performance comparison of our channel-estimation-based detector with decision feedback equalization, which uses the perfect channel information.

CHANNEL ESTIMATION OF MULTI-RATE DS-CDMA SIGNALS IN SLOW FADING MULTIPATH CHANNELS

In Direct-Sequence Code Division Multiple Access(DS-CDMA) mobile communi-cation systems, it is very important to obtain accurate estimation of the channel parameters,especially that of the propagation delay. But the near-far problem may make the estimationcomplicated and can degrade the estimation performance significantly. In this paper, an efficientMaximum Likelihood (ML) method is presented for channel parameter estimation of multi-rateDS-CDMA systems in slow fading multipath channels in a near-far scenario. The algorithmextended the ML approach to multi-rate DS-CDMA systems, and proposes decomposing a multi-dimensional optimization problem into a series of one-dimensional optimization and has improvedcomputational efficiency. Theoretical analysis and numerical examples show that the estimatorproposed is effective and near-far resistant.

A PILOT-ASSISTED NON-CYCLIC PREFIXED MIMO-SCFDE SYSTEM

A non-Cyclic Prefixed Multiple-Input Multiple-Output Single-Carrier Frequency-Domain Equalization (non-CP MIMO-SCFDE) system based on a recursive algorithm of Joint Channel Es- timation and Data Detection (recursive-JCEDD) is proposed in this paper. Unlike the traditional CP MIMO-SCFDE system, the transmitted block of the proposed system is designed in the way that block-type pilot sequences and Single-Carrier (SC) information sequences have been arranged alter- nately without any cyclic prefix before each SC information sequence. Moreover, a recursive-JCEDD algorithm based on interference cancellation is proposed for the corresponding receivers. Simulation results show that the Bit Error Rate (BER) of the proposed system based on the recursive-JCEDD algorithm is lower than traditional CP MIMO-SCFDE or MIMO-OFDM with channel estimation for more than 0.5 dB.