This paper considers the design of iterative receivers for space-frequencyblock-coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in unknown wirelessdispersive fading channels. An iterative joint ch...This paper considers the design of iterative receivers for space-frequencyblock-coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in unknown wirelessdispersive fading channels. An iterative joint channel estimation and symbol detection algorithm isderived. In the algorithm, the channel estimator performs alternately in two modes. During thetraining mode, the channel state information (CSI) is obtained by a discrete-Fourier-transform-basedchannel estimator and the noise variance and covariance matrix of the channel response is estimatedby the proposed method. In the data transmission mode, the CSI and transmitted data is obtainediteratively. In order to suppress the error propagation caused by a random error in identifyingsymbols, a simple error propagation detection criterion is proposed and an adaptive training schemeis applied to suppress the error propagation. Both theoretical analysis and simulation results showthat this algorithm gives better bit-error-rate performance and saves the overhead of OFDM systems.展开更多
A superimposed training （ST） based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing （OFDM） channel while improving...A superimposed training （ST） based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing （OFDM） channel while improving bandwidth transmission efficiency. A periodic low power training sequence is superimposed on the information sequence at the transmitter. The channel parameters can be estimated without consuming any extra system bandwidth, but an unknown information sequence can interfere with the ST channel estimation method, so in this paper, an iterative method was adopted to improve estimation performance. An underwater acoustic channel＇s properties include large channel dimensions and a sparse structure, so a matching pursuit （MP） algorithm was used to estimate the nonzero taps, allowing the performance loss caused by additive white Gaussian noise （AWGN） to be reduced. The results of computer simulations show that the proposed method has good channel estimation performance and can reduce the peak-to-average ratio of the OFDM channel as well.展开更多
Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a...Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a cyclic prefix and reference signal. However, the DCS-based channel estimation requires diversity sequences instead of UW. In this paper, we proposed a novel method that employs a training sequence(TS) whose duration time is slightly longer than the maximum delay spread time. Based on proposed TS, the DCS approach perform perfectly in multipath channel estimation. Meanwhile, a cyclic prefix construct could be formed, which reduces the complexity of the frequency domain equalization(FDE) directly. Simulation results demonstrate that, by using the method of simultaneous orthogonal matching pursuit(SOMP), the required channel overhead has been reduced thanks to the proposed TS.展开更多
Data Dependent Superimposed Training(DDST) scheme outperforms the traditional su-perimposed training by fully canceling the effects of unknown data in channel estimator.In DDST,however,the channel estimation accuracy ...Data Dependent Superimposed Training(DDST) scheme outperforms the traditional su-perimposed training by fully canceling the effects of unknown data in channel estimator.In DDST,however,the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence,which is the motivation of this research.In general,for DDST,there is a tradeoff between the channel estimation accuracy and the data detection reliability,i.e.,the more accurate the channel estimation,the more reliable the data detection;on the other hand,the more accurate the channel estimation,the more demanding on the power consumption of training sequence,which in turn leads to the less reliable data detection.In this paper,the relationship between the Signal-to-Noise Ratio(SNR) of the data detector and the training sequence power is analyzed.The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the detector.Analysis and simulation results show that for a fixed transmit power,the SNR and the Symbol Error Rate(SER) of detector vary nonlinearly with the increasing of training sequence power,and there exists an optimal power ratio,which accords with the derived optimal power ratio,among the data and training sequence.展开更多
文摘This paper considers the design of iterative receivers for space-frequencyblock-coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in unknown wirelessdispersive fading channels. An iterative joint channel estimation and symbol detection algorithm isderived. In the algorithm, the channel estimator performs alternately in two modes. During thetraining mode, the channel state information (CSI) is obtained by a discrete-Fourier-transform-basedchannel estimator and the noise variance and covariance matrix of the channel response is estimatedby the proposed method. In the data transmission mode, the CSI and transmitted data is obtainediteratively. In order to suppress the error propagation caused by a random error in identifyingsymbols, a simple error propagation detection criterion is proposed and an adaptive training schemeis applied to suppress the error propagation. Both theoretical analysis and simulation results showthat this algorithm gives better bit-error-rate performance and saves the overhead of OFDM systems.
基金Supported by the National Natural Science Foundation of China under Grant No.60572039
文摘A superimposed training （ST） based channel estimation method is presented that provides accurate estimation of a sparse underwater acoustic orthogonal frequency-division multiplexing （OFDM） channel while improving bandwidth transmission efficiency. A periodic low power training sequence is superimposed on the information sequence at the transmitter. The channel parameters can be estimated without consuming any extra system bandwidth, but an unknown information sequence can interfere with the ST channel estimation method, so in this paper, an iterative method was adopted to improve estimation performance. An underwater acoustic channel＇s properties include large channel dimensions and a sparse structure, so a matching pursuit （MP） algorithm was used to estimate the nonzero taps, allowing the performance loss caused by additive white Gaussian noise （AWGN） to be reduced. The results of computer simulations show that the proposed method has good channel estimation performance and can reduce the peak-to-average ratio of the OFDM channel as well.
基金support by National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAK05B01)
文摘Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a cyclic prefix and reference signal. However, the DCS-based channel estimation requires diversity sequences instead of UW. In this paper, we proposed a novel method that employs a training sequence(TS) whose duration time is slightly longer than the maximum delay spread time. Based on proposed TS, the DCS approach perform perfectly in multipath channel estimation. Meanwhile, a cyclic prefix construct could be formed, which reduces the complexity of the frequency domain equalization(FDE) directly. Simulation results demonstrate that, by using the method of simultaneous orthogonal matching pursuit(SOMP), the required channel overhead has been reduced thanks to the proposed TS.
基金the National Natural Science Foundation of China(NSFC)(No.60472089)
文摘Data Dependent Superimposed Training(DDST) scheme outperforms the traditional su-perimposed training by fully canceling the effects of unknown data in channel estimator.In DDST,however,the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence,which is the motivation of this research.In general,for DDST,there is a tradeoff between the channel estimation accuracy and the data detection reliability,i.e.,the more accurate the channel estimation,the more reliable the data detection;on the other hand,the more accurate the channel estimation,the more demanding on the power consumption of training sequence,which in turn leads to the less reliable data detection.In this paper,the relationship between the Signal-to-Noise Ratio(SNR) of the data detector and the training sequence power is analyzed.The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the detector.Analysis and simulation results show that for a fixed transmit power,the SNR and the Symbol Error Rate(SER) of detector vary nonlinearly with the increasing of training sequence power,and there exists an optimal power ratio,which accords with the derived optimal power ratio,among the data and training sequence.
