Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which ent...Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.展开更多
For reducing the inter-user interference in multi-user multiple-input multiple-output（MU-MIMO） wireless communication systems,e.g.,MIMO-orthogonal frequency division multiplexing（MIMO-OFDM） systems,it is often des...For reducing the inter-user interference in multi-user multiple-input multiple-output（MU-MIMO） wireless communication systems,e.g.,MIMO-orthogonal frequency division multiplexing（MIMO-OFDM） systems,it is often desirable to the complex preprocessing at the transmitter.This paper proposes a multi-user beamforming algorithm with sub-codebook selection.Based on the minimal leakage criterion,the codebook selection,limited feed-forward and minimum mean square error（MMSE） detection are combined in the proposed algorithm.This avoids the complex channel matrix decomposition and inversion.Consequently,the computational complexity at the transmitter is significantly reduced.Simulation results show that the proposed algorithm performs better than existing beamforming algorithms.展开更多
In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic su...In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.展开更多
The optimal design of training sequences for channel estimation in multiple-input multiple-output （MIMO） systems under spatially correlated fading is considered. The channel is assumed to be a block-fading model wit...The optimal design of training sequences for channel estimation in multiple-input multiple-output （MIMO） systems under spatially correlated fading is considered. The channel is assumed to be a block-fading model with spatial correlation known at both the transmitter and the receiver. To minimize the channel estimation error, optimal training sequences are designed to exploit full information of the spatial correlation under the criterion of minimum mean square error （MMSE）. It is investigated that the spatial correlation is helpful to decrease the estimation error and the proposed training sequences have good performance via simulations.展开更多
An optimal minimum mean square error successive interference cancellation （OMMSE SIC） scheme for Groupwise space-time block coding （G-STBC） multiple-input multiple-output （MIMO） systems is presented. In such a s...An optimal minimum mean square error successive interference cancellation （OMMSE SIC） scheme for Groupwise space-time block coding （G-STBC） multiple-input multiple-output （MIMO） systems is presented. In such a system, transmit antennas are partitioned into several STBC encoding groups and each group transmits independent data stream which is individually STBC encoded. On the receiver side, by exploring the temporal constraint provided by STBC, an equivalent channel model similar to the one in standard vertical Bell laboratories layered space-time （V-BLAST） systems is generated. Then OMMSE SIC algorithm is performed to detect all the transmitted information. Simulation compares the proposed scheme with non-ordering MMSE SIC scheme and the corresponding equal data rate scheme in V-BLAST systems with the same receive antennas＇ number. Result shows that the proposed scheme has better performance than non-ordering MMSE SIC scheme and by introducing more transmit antennas and adopting the OMMSE SIC scheme, better performance also can be achieved than corresponding V-BLAST systems.展开更多
A low complexity Per-Antenna Power Control （PAPC） approach based on Minimum Mean Squared Error （MMSE） detection for V-BLAST is proposed in this paper. The PAPC approach is developed for minimizing the Bit Error Ra...A low complexity Per-Antenna Power Control （PAPC） approach based on Minimum Mean Squared Error （MMSE） detection for V-BLAST is proposed in this paper. The PAPC approach is developed for minimizing the Bit Error Rate （BER） averaged over all substreams when the data throughput and the total transmit power keep constant over time. Simulation results show that the Power-controlled V-BLAST （P-BLAST） outperforms the conventional V-BLAST in terms of BER performance with MMSE detector, especially in presence of high spatial correlation between antennas. However, the additional complexity for P-BLAST is not high. When MMSE detector is adopted, the P-BLAST can achieve a comparable BER performance to that of conventional V-BLAST with Maximum Likelihood （ML） detector but with low complexity.展开更多
A novel practical codebook-precoding multiple-input multiple-output（MIMO） system based on signal space diversity（SSD） with the minimum mean squared error（MMSE）receiver is proposed.This scheme utilizes rotation m...A novel practical codebook-precoding multiple-input multiple-output（MIMO） system based on signal space diversity（SSD） with the minimum mean squared error（MMSE）receiver is proposed.This scheme utilizes rotation modulation and space-time-frequency component interleaving.A novel precoding matrix selection criterion to maximize the average signal to interference plus noise ratio（SINR） is also put forward for the proposed scheme,which has a larger average mutual information（AMI）.Based on the AMI- maximization criterion,the optimal rotation angles for the proposed system are also investigated.The new scheme can make full use of space-time-frequency diversity and signal space diversity,and exhibit high spectral efficiency and high reliability in fading channels.Simulation results show that the proposed scheme greatly outperforms the conventional bit- interleaved coded modulation（BICM） MIMO-orthogonal frequency division multiplexing（OFDM） scheme without SSD,which is up to4.5 dB signal-to-noise ratio（SNR） gain.展开更多
Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components, decorrelation based on covariance matrix fact...Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components, decorrelation based on covariance matrix factorization is required in the previous methods in order to perform sequential updates properly. A new sequential processing method, which carries out the sequential updates directly using the correlated measurement components, is proposed. And a typical sequential processing example is investigated, where the converted position measure- ments are used to estimate target states by standard Kalman filtering equations and the converted Doppler measurements are then incorporated into a minimum mean squared error （MMSE） estimator with the updated cross-covariance involved to account for the correlated errors. Numerical simulations demonstrate the superiority of the proposed new sequential processing in terms of better accuracy and consistency than the conventional sequential filter based on measurement decorrelation.展开更多
基金supported by National Natural Science Foundation of China(62371225,62371227)。
文摘Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.
基金support by the National Natural Science Foundation of China (60702060)the 111 Project
文摘For reducing the inter-user interference in multi-user multiple-input multiple-output（MU-MIMO） wireless communication systems,e.g.,MIMO-orthogonal frequency division multiplexing（MIMO-OFDM） systems,it is often desirable to the complex preprocessing at the transmitter.This paper proposes a multi-user beamforming algorithm with sub-codebook selection.Based on the minimal leakage criterion,the codebook selection,limited feed-forward and minimum mean square error（MMSE） detection are combined in the proposed algorithm.This avoids the complex channel matrix decomposition and inversion.Consequently,the computational complexity at the transmitter is significantly reduced.Simulation results show that the proposed algorithm performs better than existing beamforming algorithms.
基金supported by the National Natural Science Foundation of China (6096200161071088)+2 种基金the Natural Science Foundation of Fujian Province of China (2012J05119)the Fundamental Research Funds for the Central Universities (11QZR02)the Research Fund of Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (21104)
文摘In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.
基金the National Science Foundation for Distinguished Young Scholars (60725105)the SixthProject of the Key Project of National Nature Science Foundation of China (60496316)+2 种基金the National "863" Project (2007AA012288)the National Nature Science Foundation of China (60572146)the "111" Project (B08038).
文摘The optimal design of training sequences for channel estimation in multiple-input multiple-output （MIMO） systems under spatially correlated fading is considered. The channel is assumed to be a block-fading model with spatial correlation known at both the transmitter and the receiver. To minimize the channel estimation error, optimal training sequences are designed to exploit full information of the spatial correlation under the criterion of minimum mean square error （MMSE）. It is investigated that the spatial correlation is helpful to decrease the estimation error and the proposed training sequences have good performance via simulations.
文摘An optimal minimum mean square error successive interference cancellation （OMMSE SIC） scheme for Groupwise space-time block coding （G-STBC） multiple-input multiple-output （MIMO） systems is presented. In such a system, transmit antennas are partitioned into several STBC encoding groups and each group transmits independent data stream which is individually STBC encoded. On the receiver side, by exploring the temporal constraint provided by STBC, an equivalent channel model similar to the one in standard vertical Bell laboratories layered space-time （V-BLAST） systems is generated. Then OMMSE SIC algorithm is performed to detect all the transmitted information. Simulation compares the proposed scheme with non-ordering MMSE SIC scheme and the corresponding equal data rate scheme in V-BLAST systems with the same receive antennas＇ number. Result shows that the proposed scheme has better performance than non-ordering MMSE SIC scheme and by introducing more transmit antennas and adopting the OMMSE SIC scheme, better performance also can be achieved than corresponding V-BLAST systems.
基金This project was supported by the National Natural Science Foundation of China ( 60496314).
文摘A low complexity Per-Antenna Power Control （PAPC） approach based on Minimum Mean Squared Error （MMSE） detection for V-BLAST is proposed in this paper. The PAPC approach is developed for minimizing the Bit Error Rate （BER） averaged over all substreams when the data throughput and the total transmit power keep constant over time. Simulation results show that the Power-controlled V-BLAST （P-BLAST） outperforms the conventional V-BLAST in terms of BER performance with MMSE detector, especially in presence of high spatial correlation between antennas. However, the additional complexity for P-BLAST is not high. When MMSE detector is adopted, the P-BLAST can achieve a comparable BER performance to that of conventional V-BLAST with Maximum Likelihood （ML） detector but with low complexity.
基金supported by the National Natural Science Foundation of China(61171101)the Fundamental Research Funds for the Central Universitiesthe 2014 Doctorial Innovation Fund of Beijing University of Posts and Telecommunications(CX201426)
文摘A novel practical codebook-precoding multiple-input multiple-output（MIMO） system based on signal space diversity（SSD） with the minimum mean squared error（MMSE）receiver is proposed.This scheme utilizes rotation modulation and space-time-frequency component interleaving.A novel precoding matrix selection criterion to maximize the average signal to interference plus noise ratio（SINR） is also put forward for the proposed scheme,which has a larger average mutual information（AMI）.Based on the AMI- maximization criterion,the optimal rotation angles for the proposed system are also investigated.The new scheme can make full use of space-time-frequency diversity and signal space diversity,and exhibit high spectral efficiency and high reliability in fading channels.Simulation results show that the proposed scheme greatly outperforms the conventional bit- interleaved coded modulation（BICM） MIMO-orthogonal frequency division multiplexing（OFDM） scheme without SSD,which is up to4.5 dB signal-to-noise ratio（SNR） gain.
基金supported by the National Natural Science Foundation of China(6120131161132005)the Aerospace Science Foundation of China(20142077010)
文摘Sequential measurement processing is of benefit to both estimation accuracy and computational efficiency. When the noises are correlated across the measurement components, decorrelation based on covariance matrix factorization is required in the previous methods in order to perform sequential updates properly. A new sequential processing method, which carries out the sequential updates directly using the correlated measurement components, is proposed. And a typical sequential processing example is investigated, where the converted position measure- ments are used to estimate target states by standard Kalman filtering equations and the converted Doppler measurements are then incorporated into a minimum mean squared error （MMSE） estimator with the updated cross-covariance involved to account for the correlated errors. Numerical simulations demonstrate the superiority of the proposed new sequential processing in terms of better accuracy and consistency than the conventional sequential filter based on measurement decorrelation.
