Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete diction...Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete dictionary with a set of non-orthogonal bases. In this paper, we proposed a new approach to optimize dictionaries by decreasing the average measure of the mutual coherence of the effective dictionary. A fixed link between the average mutual coherence and the CS perforrmnce is indicated by designing three factors： operating bandwidth, the number of pilot subcarriers, and coherence bandwidth. Both the Orthogonal Matching Pursuit （OMP） and the Basis Pursuit De-Noising （BPDN） are compared to the Dantzig Selector （DS） for different Signal Noise Ratio （SNR） and shown to benefit from the newly designed dictionary. Nurnerical sinmlations and experimental data of an OFDM receiver are used to evaluate the proposed method in comparison with the conventional LeastSquare （LS） estirmtor. The results show that the dictionary with a better condition considerably improves the perforrmnce of the channel estimation.展开更多
Acquisition of accurate channel state information （CSI） at transmitters results in a huge pilot overhead in massive multiple input multiple output （MIMO） systems due to the large number of antennas in the base sta...Acquisition of accurate channel state information （CSI） at transmitters results in a huge pilot overhead in massive multiple input multiple output （MIMO） systems due to the large number of antennas in the base station （BS）. To reduce the overwhelming pilot overhead in such systems, a structured joint channel estimation scheme employing compressed sensing （CS） theory is proposed. Specifically, the channel sparsity in the angular domain due to the practical scattering environment is analyzed, where common sparsity and individual sparsity structures among geographically neighboring users exist in multi-user massive MIMO systems. Then, by equipping each user with multiple antennas, the pilot overhead can be alleviated in the framework of CS and the channel estimation quality can be improved. Moreover, a structured joint matching pursuit （SJMP） algorithm at the BS is proposed to jointly estimate the channel of users with reduced pilot overhead. Furthermore, the probability upper bound of common support recovery and the upper bound of channel estimation quality using the proposed SJMP algorithm are derived. Simulation results demonstrate that the proposed SJMP algorithm can achieve a higher system performance than those of existing algorithms in terms of pilot overhead and achievable rate.展开更多
This paper deals with optimal training design and placement over multiple orthogonal frequency division multiplexing(OFDM) symbols for the least squares(LS) channel estimation in multiple-input multipleoutput(MIMO) OF...This paper deals with optimal training design and placement over multiple orthogonal frequency division multiplexing(OFDM) symbols for the least squares(LS) channel estimation in multiple-input multipleoutput(MIMO) OFDM systems.First,the optimal pilot sequences over multiple OFDM symbols are derived by co-cyclic Jacket matrices based on the minimum mean square error(MSE) of the LS channel estimation.Then,an enhanced channel estimation method using sliding window is proposed to improve further the performance for the optimal pilot sequences in fast-varying channels.Simulation results show that the enhancedmethod can efficiently improve the performances for the optimal pilot sequences over two and four OFDM symbols,especially in fast-varying channels.展开更多
基金Acknowledgements This work was supported by the National Science Foundation of China under Grant No. 60976065. The authors would like to thank the anonymous reviewers for comments that helped improve the paper.
文摘Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete dictionary with a set of non-orthogonal bases. In this paper, we proposed a new approach to optimize dictionaries by decreasing the average measure of the mutual coherence of the effective dictionary. A fixed link between the average mutual coherence and the CS perforrmnce is indicated by designing three factors： operating bandwidth, the number of pilot subcarriers, and coherence bandwidth. Both the Orthogonal Matching Pursuit （OMP） and the Basis Pursuit De-Noising （BPDN） are compared to the Dantzig Selector （DS） for different Signal Noise Ratio （SNR） and shown to benefit from the newly designed dictionary. Nurnerical sinmlations and experimental data of an OFDM receiver are used to evaluate the proposed method in comparison with the conventional LeastSquare （LS） estirmtor. The results show that the dictionary with a better condition considerably improves the perforrmnce of the channel estimation.
基金Project supported by the Fundamental Research Funds for the Cen- tral Universities （No. HIT.MKSTISP.2016 13） and the National Natural Science Foundation of China （No. 61671176）
文摘Acquisition of accurate channel state information （CSI） at transmitters results in a huge pilot overhead in massive multiple input multiple output （MIMO） systems due to the large number of antennas in the base station （BS）. To reduce the overwhelming pilot overhead in such systems, a structured joint channel estimation scheme employing compressed sensing （CS） theory is proposed. Specifically, the channel sparsity in the angular domain due to the practical scattering environment is analyzed, where common sparsity and individual sparsity structures among geographically neighboring users exist in multi-user massive MIMO systems. Then, by equipping each user with multiple antennas, the pilot overhead can be alleviated in the framework of CS and the channel estimation quality can be improved. Moreover, a structured joint matching pursuit （SJMP） algorithm at the BS is proposed to jointly estimate the channel of users with reduced pilot overhead. Furthermore, the probability upper bound of common support recovery and the upper bound of channel estimation quality using the proposed SJMP algorithm are derived. Simulation results demonstrate that the proposed SJMP algorithm can achieve a higher system performance than those of existing algorithms in terms of pilot overhead and achievable rate.
基金the National Natural Science Foundation of China (Nos. 60332030 and 60625103)the Science and Technology Commission of Shanghai Municipality (STCSM) (No. 05DZ22102)the National High Technology Research and Development Program(863) of China (No. 2007AA01Z237)
文摘This paper deals with optimal training design and placement over multiple orthogonal frequency division multiplexing(OFDM) symbols for the least squares(LS) channel estimation in multiple-input multipleoutput(MIMO) OFDM systems.First,the optimal pilot sequences over multiple OFDM symbols are derived by co-cyclic Jacket matrices based on the minimum mean square error(MSE) of the LS channel estimation.Then,an enhanced channel estimation method using sliding window is proposed to improve further the performance for the optimal pilot sequences in fast-varying channels.Simulation results show that the enhancedmethod can efficiently improve the performances for the optimal pilot sequences over two and four OFDM symbols,especially in fast-varying channels.
