Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel E...Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.展开更多
针对多输入多输出滤波器组多载波(Multiple Input Multiple Output Filter Bank Multi Carrier,MIMO-FBMC)系统的同步问题,提出了一种基于训练序列(Training Sequence)的同步技术改进算法。该算法利用ZC(Zadoff-Chu)序列构造训练序列,...针对多输入多输出滤波器组多载波(Multiple Input Multiple Output Filter Bank Multi Carrier,MIMO-FBMC)系统的同步问题,提出了一种基于训练序列(Training Sequence)的同步技术改进算法。该算法利用ZC(Zadoff-Chu)序列构造训练序列,并将训练序列分为3部分,第1、2部分采用相同的训练符号,第3部分2次复制第1部分的后半部分,多天线上的训练序列利用循环移位来实现,再分别利用它们的相关性来进行定时同步和频率偏移的估计。仿真结果表明,与Schmidl及Minn算法相比,改进的训练序列结构和算法减小了计算量,同时可获得更高的定时性能和更低的误码率。展开更多
Filter-bank multicarrier （FBMC） with offset quadrature amplitude modulation （OQAM） is a candidate waveform for future wireless communications due to its advantages over orthogonal frequency division multiplexing ...Filter-bank multicarrier （FBMC） with offset quadrature amplitude modulation （OQAM） is a candidate waveform for future wireless communications due to its advantages over orthogonal frequency division multiplexing （OFDM） systems. However, because of or-thogonality in real field and the presence of imaginary intrinsic interference, channel estimation in FBMC is not as straightforward as OFDM systems especially in multiple antenna scenarios. In this paper, we propose a channel estimation method which employs intrinsic interference cancellation at the transmitter side. The simulation results show that this method has less pilot overhead, less peak to average power ratio （PAPR）, better bit error rate （BER）, and better mean square error （MSE） performance compared to the well-known intrinsic approximation methods （IAM）.展开更多
基金supported by National Natural Science Foundation of China under Grant Nos.61901409 and 61961013Jiangxi Provincial Natural Science Foundation under Grant No.20202BABL212001Open Project of State Key Laboratory of Marine Resources Utilization in South China Sea under Grant No.MRUKF2021034.
文摘Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.
文摘针对多输入多输出滤波器组多载波(Multiple Input Multiple Output Filter Bank Multi Carrier,MIMO-FBMC)系统的同步问题,提出了一种基于训练序列(Training Sequence)的同步技术改进算法。该算法利用ZC(Zadoff-Chu)序列构造训练序列,并将训练序列分为3部分,第1、2部分采用相同的训练符号,第3部分2次复制第1部分的后半部分,多天线上的训练序列利用循环移位来实现,再分别利用它们的相关性来进行定时同步和频率偏移的估计。仿真结果表明,与Schmidl及Minn算法相比,改进的训练序列结构和算法减小了计算量,同时可获得更高的定时性能和更低的误码率。
基金supported by ZTE Industry-Academia-Research Cooperation Funds under Grant No.Surrey-Ref-9953
文摘Filter-bank multicarrier （FBMC） with offset quadrature amplitude modulation （OQAM） is a candidate waveform for future wireless communications due to its advantages over orthogonal frequency division multiplexing （OFDM） systems. However, because of or-thogonality in real field and the presence of imaginary intrinsic interference, channel estimation in FBMC is not as straightforward as OFDM systems especially in multiple antenna scenarios. In this paper, we propose a channel estimation method which employs intrinsic interference cancellation at the transmitter side. The simulation results show that this method has less pilot overhead, less peak to average power ratio （PAPR）, better bit error rate （BER）, and better mean square error （MSE） performance compared to the well-known intrinsic approximation methods （IAM）.
