Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems

Abstract

This paper develops efficient channel estimation techniques for millimeter-wave (mmWave) massive multipleinput multiple-output (MIMO) systems under practical hardware limitations, including an arbitrary array geometry and a hybrid hardware structure. Taking on an angle-based approach, this work adopts a generalized array manifold separation approach via Jacobi-Anger approximation, which transforms a non-ideal, non-uniform array manifold into a virtual array domain with a desired uniform geometric structure to facilitate super-resolution angle estimation and channel acquisition. Accordingly, structurebased optimization techniques are developed to effectively estimate both the channel covariance and the instantaneous channel state information (CSI) within a short sensing time. In particular, the difference in time-variation of channel path angles and path gains is capitalized to design a two-step CSI estimation scheme that can quickly sense fading channels. Theoretical results are provided on the fundamental limits of the proposed technique in terms of sample efficiency. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. Other related issues such as spuriouspeak cancellation in nonuniform linear arrays and extensions to higher-dimensional cases are also discussed. Simulations testify the effectiveness of the proposed approaches in hybrid mmWave massive MIMO systems with arbitrary arrays.

Low Complexity Beamspace Super Resolution for DOA Estimation of Linear Array

“…Considering a narrowband millimeter-wave massive MIMO system with N antennas ULA at base station and a single antenna mobile phone, the uplink channel can be expressed as [11]…”

“…However, the discrete sparse recovery methods may suffer from the off-grid sources problem, a.k.a. power leakage effect [11].…”

“…For beamspace processing cases, 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]. However, 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 5G, even though the dimension of the beamspace may be quite small.…”

Random SBT Precoding for Angle Estimation of mmWave Massive MIMO Systems Using Sparse Arrays Spacing

“…Many methods have been proposed to estimate AoA/AoD subject to hybrid structure [12]- [24]. In these works, the channels are assumed to be either time-invariant or timevarying.…”

“…Based on OMP, generalized block OMP is developed in [20] to take advantage of the block sparsity property of mmWave channels for better channel estimation. In the case of a time-varying channel [21]- [24], the angles remain the same while the path gains are time-varying due to fading that is caused by user movement. Such a channel model is valid if the training period is longer than the channel coherence time.…”