Efficient Angle Estimation for MIMO Systems via Redundancy Reduction Representation

Abstract

This paper proposes an efficient direction of departure (DOD) and direction of arrival (DOA) estimation method for multi-input multi-output (MIMO) systems. For uncorrelated scenarios, the redundancy of the covariance matrix is first exploited by establishing its concise representation through redundancy reduction, which transforms the original large-size covariance matrix into a smaller-size matrix without loss of useful angle information. Then, the resulting transformed matrix, which retains a salient structure, permits efficient two-dimensional (2D) angle estimators working on a reduced-size problem for DOD and DOA estimation. Compared with conventional subspace-based methods, the proposed method incorporating an appropriate 2D angle estimator is more computationally efficient and can achieve higher estimation accuracy for small numbers of snapshots and low signal-to-noise ratios, which are verified by simulation results.

Downlink Massive MIMO Channel Estimation via Deep Unrolling : Sparsity Exploitations in Angular Domain

“…It is observed that in massive MIMO scenarios, the channel exhibits sparse multipath structure due to the limited scatterings. Thus, a virtual angular domain representation is applied to characterize the MIMO channel by fixed virtual receive and transmit directions [15]- [17]:…”

“…The unrolled structure of the coarse estimation net in Network 1 is realized based on (19), where ΦT and α/q in (19) are replaced by the trainable weights W l e and θ l e in Step 3 of Network 1, respectively. Note that by considering largescale inter-frame sparsity, ( 8) is a typical l 2,1 minimization problem, thus δ α/q (•) can be directly extended to (13) and (17).…”

“…Note that the whole network structure of C-BFSJ are the same as C-BSS, thus the detailed explanation of C-BFSJ is omitted here for simplicity. C-BFSJ differs from C-BSS only in the utilization of the thresholding function (17).…”

“…Secondly, the coefficients of these selected nonzero rows corresponding to Γs are derived based on (13) or (17).…”

“…Among different types of sparse channel structures, sparsity in angular domain is a typical one that is often studied and utilized in CS-based MIMO channel estimation methods, e.g., [10]- [15]. Such sparsity stems from the fact that millimeter-wave (mmWave) propagation experiences limited scattering at BS with sparse multipaths, which induces limited angular directionalities of mmWave massive MIMO channels [15]- [17].…”