A Low-complexity Beamspace-based Method for DOA Estimation in Massive MIMO Systems

The complexity of direction-of-arrival (DOA) estimation method for massive multiple-input multiple-output (MIMO) systems is critical and extremely affects its application. To reduce the computational complexity, we propose a new low-complexity DOA estimation method based on the beamspace transformation and the improvements of propagator method named as low-complexity beamspace-based improved propagator method (LBPM). Specifically, the received signal vectors in the antenna-element space are transformed into the beamspace by employing beamforming vectors, which simplifies the array manifold so that the total dimensions of the received signal vectors are significantly reduced. Then, to further decrease the computational complexity, the linear operation of propagator method is utilized to replace the eigenvalue decomposition (EVD) of the covariance matrix. Besides, the transformed data is conjugated and rearranged to improve the precision of the proposed method. Numerical simulation results indicate that the proposed method enjoys better DOA estimation performance than that of the conventional methods in massive MIMO systems.

[1]  Michael D. Zoltowski,et al.  Multidimensional multirate DOA estimation in beamspace , 1996, IEEE Trans. Signal Process..

[2]  Andrew R Nix,et al.  Performance analysis of ESPRIT, TLS-ESPRIT and Unitary-ESPRIT algorithms for DOA estimation in a W-CDMA mobile system , 2000 .

[3]  J.-J. Fuchs.  On the use of the global matched filter for DOA estimation in the presence of correlated waveforms , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[4]  Liu Jian,et al.  Extended Propagator Method for Direction Estimation of Noncircular Signals , 2008 .

[5]  Steven M. Kay,et al.  An Importance Sampling Maximum Likelihood Direction of Arrival Estimator , 2008, IEEE Transactions on Signal Processing.

[6]  Jianzhong Zhang,et al.  Low complexity direction of arrival (DoA) estimation for 2D massive MIMO systems , 2012, 2012 IEEE Globecom Workshops.

[7]  Yaowu Shi,et al.  Joint angle-frequency estimation based on WSF using Artificial Bee Colony algorithm , 2013, 2013 IEEE Third International Conference on Information Science and Technology (ICIST).

[8]  Osama N. Alrabadi,et al.  Beamforming via Large and Dense Antenna Arrays Above a Clutter , 2013, IEEE Journal on Selected Areas in Communications.

[9]  Lin Tian,et al.  A Novel AWSF Algorithm for DOA Estimation in Virtual MIMO Systems , 2013, IEEE Journal on Selected Areas in Communications.

[10]  Jwo-Yuh Wu,et al.  A low-complexity direction-of-arrival estimation algorithm for full-dimension massive MIMO systems , 2014, 2014 IEEE International Conference on Communication Systems.

[11]  Chen Liang,et al.  Improved Tensor-MODE Based Direction-of-Arrival Estimation for Massive MIMO Systems , 2015, IEEE Communications Letters.

[12]  Bo Ai,et al.  Two-Dimension Direction-of-Arrival Estimation for Massive MIMO Systems , 2015, IEEE Access.

[13]  Chau Yuen,et al.  Energy-Efficient Downlink Transmission for Multicell Massive DAS With Pilot Contamination , 2016, IEEE Transactions on Vehicular Technology.

[14]  Said Esmail El-Khamy,et al.  A smart multi-user massive MIMO system for next G Wireless communications using evolutionary optimized antenna selection , 2017, Telecommun. Syst..

[15]  Shuai Liu,et al.  MUSIC-like direction of arrival estimation based on virtual array transformation , 2017, Signal Process..

[16]  Saba Asaad,et al.  Massive MIMO With Antenna Selection: Fundamental Limits and Applications , 2018, IEEE Transactions on Wireless Communications.

[17]  Wajih Ul Hassan,et al.  DOA Estimation for Massive MIMO System via Low-Complexity Trilinear Decomposition Method , 2019, 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP).

[18]  Xiaofei Zhang,et al.  A Higher-Order Propagator Method for 2D-DOA Estimation in Massive MIMO Systems , 2020, IEEE Communications Letters.