Joint DL and UL Channel Estimation for Millimeter Wave MIMO Systems Using Tensor Modeling

Abstract

In this paper, we address the problem of joint downlink (DL) and uplink (UL) channel estimation for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Assuming a closed-loop and multifrequency-based channel training framework in which pilot signals received by multiple antenna mobile stations (MSs) are coded and spread in the frequency domain via multiple adjacent subcarriers, we propose two tensor-based semiblind receivers by capitalizing on the multilinear structure and sparse feature of the received signal at the BS equipped with a hybrid analog-digital beamforming (HB) architecture. As a first processing stage, the joint estimation of the compressed DL and UL channel matrices can be obtained in an iterative way by means of an alternating least squares (ALS) algorithm that capitalizes on a parallel factors model for the received signals. Alternatively, for more restricted scenarios, a closed-form solution is also proposed. From the estimated effective channel matrices, the users’ channel parameters such as angles of departure (AoD), angles of arrival (AoA), and path gains are then estimated in a second processing stage by solving independent compressed sensing (CS) problems (one for each MS). In contrast to the classical approach in the literature, in which the DL and UL channel estimation problems are usually considered as two separate problems, our idea is to jointly estimate both the DL and UL channels as a single problem by concentrating most of the processing burden for channel estimation at the BS side. Simulation results demonstrate that the proposed receivers achieve a performance close to the classical approach that is applied on DL and UL communication links separately, with the advantage of avoiding complex computations for channel estimation at the MS side as well as dedicated feedback channels for each MS, which are attractive features for massive MIMO systems.

A Comprehensive Survey on Millimeter Wave Communications for Fifth-Generation Wireless Networks: Feasibility and Challenges

“…The authors of [303], formulated the AoAs/AoDs estimation into a blocksparse signal recovery problem and introduced an adaptive angle estimation algorithm to solve the problem, i.e., to estimate the AoAs/AoDs. The researchers [302] and [304] have introduced CS-based and tensor-based CE algorithms for mmWave massive MIMO systems, respectively. However, they assume the conventional framework for training-based estimation, where the downlink and uplink CE problems are decoupled, and addressed separately at the receiver and transmitter, respectively.…”

“…As the dimension of the mmWave massive MIMO channel matrix increases, matrix operations required in the CE task will induce significantly higher computational complexity in the practical realization. For example, in frequency division duplexing (FDD) mode for 5G, CE is usually performed at the power-limited UE side [304], where the computational complexity becomes a major challenge as the number channel coefficients to be estimated is large. By exploiting the spatially common sparsity and temporal correlation of massive MIMO channels, the CE dimension challenge, can be effectively reduced.…”

Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach

“…In the last decade, tensor modeling has been employed in a variety of signal processing problems [24]- [29], in particular to solve wireless communications related problems such as semi-blind receivers for MIMO systems [30], [31], channel estimation methods for cooperative communications [32], [33], direction of arrival estimation and beamforming in array processing [34]- [36], and, more recently, compressed channel estimation in massive MIMO systems [37], [38]. This paper links tensor modeling to IRS-based MIMO systems, and shows that exploiting the tensor structure of the received signals provides an effective way to solve the channel estimation problem.…”

Tensor Decompositions in Wireless Communications and MIMO Radar

“…A tensor-based minimum mean square error (MMSE) channel estimator was proposed and then by incorporating a 3D sparse representation into the tensor-based channel model, a tensor compressive sensing (tensor-CS) model is formulated by assuming that the channel is compressively sampled in space (radio-frequency chains), time (symbol periods), and frequency (pilot subcarriers), which is used as the basis for the formulation of a tensororthogonal matching-pursuit (T-OMP) estimator. The work [100] addressed the problem of joint downlink (DL) and uplink (UL) channel estimation for millimeter wave mmWave MIMO systems using a tensor modeling approach. Assuming a closedloop and multifrequency-based channel training framework, the algorithms developed therein jointly estimated both the DL and UL channels by concentrating most of the processing burden for channel estimation at the BS side.…”