Abstract
Beamspace processing has become much attractive in recent radar and wireless communication applications, since the advantages of complexity reduction and of performance improvements in array signal processing. In this paper, we concentrate on the beamspace DOA estimation of linear array via atomic norm minimization (ANM). The existed generalized linear spectrum estimation based ANM approaches suffer from the high computational complexity for large scale array, since their complexity depends upon the number of sensors. To deal with this problem, we develop a low dimensional semidefinite programming (SDP) implementation of beamspace atomic norm minimization (BS-ANM) approach for DFT beamspace based on the super resolution theory on the semi-algebraic set. Then, a computational efficient iteration algorithm is proposed based on alternating direction method of multipliers (ADMM) approach. We develop the covariance based DOA estimation methods via BS-ANM and apply the BS-ANM based DOA estimation method to the channel estimation problem for massive MIMO systems. Simulation results demonstrate that the proposed methods exhibit the superior performance compared to the state-of-the-art counterparts.
Highlights
Direction-of-Arrive (DOA) estimation is an important topic in many applications, such as radar, sonar, and wireless communication
Due to the semidefinite programming (SDP) based methods with the high computational burden, we propose a fast implementation of base station (BS)-atomic norm minimization (ANM) approach via alternating direction method of multipliers (ADMM)
Based on the beamspace atomic norm minimization (BS-ANM) approaches proposed in Sections 3 and 4, we develop the covariance matrix based DOA estimation algorithm in free space and the channel estimation method for massive MIMO
Summary
Direction-of-Arrive (DOA) estimation is an important topic in many applications, such as radar, sonar, and wireless communication. The existed ANM methods are usually based on the generalized line spectral estimation (GL) framework [17], which regards the beamspace DOA estimation problem as the line spectral estimation problem with linear mapping constraints, which can be solved by the conventional ANM approaches [11,18,19,20] These methods focus on recovering the signal on the sensors of the receiver, which may yield high computational burden (since high dimensional SDP formulation) in the case of large number of sensors, e.g., millimeter-wave massive MIMO system for. We define the beamspace atomic norm and propose the low dimensional SDP implementations based on the super resolution theory on the semi-algebraic set. Sup {} denote the infimum and supremum of a set, respectively
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