Millimeter wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems generally adopt hybrid precoding combining digital and analog precoder as an alternative to full digital precoding to reduce RF chains and energy consumption. In order to balance the relationship between spectral efficiency, energy efficiency and hardware complexity, the hybrid-connected system structure should be adopted, and then the solution process of hybrid precoding can be simplified by decomposing the total achievable rate into several sub-rates. However, the singular value decomposition (SVD) incurs high complexity in calculating the optimal unconstrained hybrid precoder for each sub-rate. Therefore, this paper proposes PAST, a low complexity hybrid precoding algorithm based on projection approximate subspace tracking. The optimal unconstrained hybrid precoder of each sub-rate is estimated with the PAST algorithm, which avoids the high complexity process of calculating the left and right singular vectors and singular value matrix by SVD. Simulations demonstrate that PAST matches the spectral efficiency of SVD-based hybrid precoding in full-connected (FC), hybrid-connected (HC) and sub-connected (SC) system structure. Moreover, the superiority of PAST over SVD-based hybrid precoding in terms of complexity and increases with the number of transmitting antennas.

Direction-of-arrival (DOA) estimation based on subspace methods has collected much interest over a few decades, and adaptive DOA estimation with rapidly changing parameters will be necessary for wireless communications. This paper is concerned with a new subspace tracking scheme by using an accelerated LMS and RLS algorithms for time-varying parameters. The proposed accelerated adaptive algorithms are based on the internal model principle by approximately expressing the changing parameters by an expansion of polynomial time functions. Thus its application to DOA estimation based on the MUSIC and MODE schemes is presented and the effectiveness is validated in numerical simulations.

To understand radio propagation structures and consider signal recovering techniques in mobile communications, it is most effective to estimate the signal parameters (e.g., DOA) of individual incoming waves. Also, in radar systems, it is required to discriminate the desired signal from interference. As one of the high-resolution DOA estimators, MUSIC and ESPRIT have attracted considerable attention in recent years. They need the eigenvectors of the correlation matrix and therefore we have to execute the EVD (eigenvalue decomposition) of correlation matrix. However, the EVD generally brings us a heavy computational load and as a result it is difficult to realize the real-time DOA estimator, which will be useful as a multibeam-forming algorithm for adaptive antennas. This paper focuses on MUSIC and ESPRIT using subspace tracking methods, such as BiSVD, PAST, and PASTd, to carry out iterative DOA estimation. Then, they are compared through computer simulation. Adaptive beamforming based on DCMP and MLM is also mentioned and an example is shown.