Channel estimation is very important for MIMO (Multiple Input Multiple Output) OFDM (Or-thogonal Frequency Division Multiplexing) systems, but its precision is reduced due to the noise in channel. In this letter, circ...Channel estimation is very important for MIMO (Multiple Input Multiple Output) OFDM (Or-thogonal Frequency Division Multiplexing) systems, but its precision is reduced due to the noise in channel. In this letter, circularly slipping window is introduced to resist the noise. It can be proved by simulation that with the same channel model, optimal slipping window length is the same with different vehicle speed. MSE (Minimum Square Error) of channel is greatly reduced with circularly slipping window, and performance of the system is closed to that with correct channel estimation.展开更多
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 decompositio...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.展开更多
The combination of fingerprint positioning and 5G(the 5th Generation Mobile Communication Technology)offers broader application prospects for indoor positioning technology,but also brings challenges in real-time perfo...The combination of fingerprint positioning and 5G(the 5th Generation Mobile Communication Technology)offers broader application prospects for indoor positioning technology,but also brings challenges in real-time performance.In this paper,we propose a fingerprint positioning method based on a deep convolutional neural network(DCNN)using a classification approach in a single-base station scenario for massive multiple input multiple outputorthogonal frequency division multiplexing(MIMO-OFDM)systems.We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation.The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences.First,the DCNN model is used to determine the sub-area to which the mobile terminal belongs,and then the weighted K-nearest neighbor(WKNN)matching algorithm is used to estimate the position within the sub-area.The positioning performance is simulated in a DeepMIMO indoor environment,showing that the classification DCNN method reduces the positioning time by 77.05%compared to the non-classification method,with only a 1.08%increase in average positioning error.展开更多
文摘Channel estimation is very important for MIMO (Multiple Input Multiple Output) OFDM (Or-thogonal Frequency Division Multiplexing) systems, but its precision is reduced due to the noise in channel. In this letter, circularly slipping window is introduced to resist the noise. It can be proved by simulation that with the same channel model, optimal slipping window length is the same with different vehicle speed. MSE (Minimum Square Error) of channel is greatly reduced with circularly slipping window, and performance of the system is closed to that with correct channel estimation.
文摘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.
基金supported by the National Key Research and Development Program of China(No.2022YFC3801000)the Fundamental Research Funds for the Central Universities(No.2242022k60001,2242023K40015).
文摘The combination of fingerprint positioning and 5G(the 5th Generation Mobile Communication Technology)offers broader application prospects for indoor positioning technology,but also brings challenges in real-time performance.In this paper,we propose a fingerprint positioning method based on a deep convolutional neural network(DCNN)using a classification approach in a single-base station scenario for massive multiple input multiple outputorthogonal frequency division multiplexing(MIMO-OFDM)systems.We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation.The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences.First,the DCNN model is used to determine the sub-area to which the mobile terminal belongs,and then the weighted K-nearest neighbor(WKNN)matching algorithm is used to estimate the position within the sub-area.The positioning performance is simulated in a DeepMIMO indoor environment,showing that the classification DCNN method reduces the positioning time by 77.05%compared to the non-classification method,with only a 1.08%increase in average positioning error.
