Bayesian Optimal Data Detector for Hybrid mmWave MIMO-OFDM Systems With Low-Resolution ADCs

Abstract

Hybrid analog-digital precoding architectures and low-resolution analog-to-digital converter (ADC) receivers are two solutions to reduce hardware cost and power consumption for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication systems with large antenna arrays. In this study, we consider a mmWave MIMO-OFDM receiver with a generalized hybrid architecture in which a small number of radio-frequency (RF) chains and low-resolution ADCs are employed simultaneously. Owing to the strong nonlinearity introduced by low-resolution ADCs, the task of data detection is challenging, particularly achieving a Bayesian optimal data detector. This study aims to fill this gap. By using generalized expectation consistent signal recovery technique, we propose a computationally efficient data detection algorithm that provides a minimum mean-square error estimate on data symbols and is extended to a mixed-ADC architecture. Considering particular structure of MIMO-OFDM channel matirx, we provide a lowcomplexity realization in which only FFT operation and matrixvector multiplications are required. Furthermore, we present an analytical framework to study the theoretical performance of the detector in the large-system limit, which can precisely evaluate the performance expressions such as mean-square error and symbol error rate. Based on this optimal detector, the potential of adding a few low-resolution RF chains and high-resolution ADCs for mixed-ADC architecture is investigated. Simulation results confirm the accuracy of our theoretical analysis and can be used for system design rapidly. The results reveal that adding a few low-resolution RF chains to original unquantized systems can obtain significant gains.

FCFGS-CV-Based Channel Estimation for Wideband mmWave Massive MIMO Systems With Low-Resolution ADCs

“…1, the decreasing point of f CV (·) clearly indicates when the minimum NMSE occurs, whereas f E (·) provides no clear indication of overfitting. In the simulation results, the GAMP [3], generalized VAMP (GVAMP) [3], GEC-SR [4], and generalized SBL (Gr-SBL) [5] algorithms for generalized linear models (GLMs) [13], [14] are adopted as benchmarks, which are state-of-the-art compressed sensing-based algorithms. Gr-SBL is configured as R AoA = 2M and R delay = 2D.…”

“…Now, the asymptotic complexity of FCFGS-CV is evaluated. Assuming that R = O(M DK) with t-iteration gradient descent method used in Line 6 of Algorithm 1, the complexities of the k-th iteration of FCFGS-CV, GAMP, GVAMP, GEC-SR, and Gr-SBL are O(M 2 N DK + M N kt), O(M 2 N DK) [9], O(M 2 N DK) [9], O(M 3 N D 2 K 2 ) [4], and O(M 3 N D 2 K 2 ) [15]. The complexities of FCFGS-CV, GAMP, and GVAMP have the same order, whereas the complexities of GEC-SR and Gr-SBL are increased by a factor of O(M DK) compared to those of FCFGS-CV, GAMP, and GVAMP.…”

“…However, GAMP and VAMP break down when the sensing matrix is ill-conditioned. The turbo principle-based generalized expectation consistent signal recovery (GEC-SR) [4] and hierarchical model-based sparse Bayesian learning (SBL) [5] algorithms are relatively robust to the condition number of the sensing matrix but require matrix inversion, which results in high complexity.…”

Channel estimation via gradient pursuit for mmWave massive MIMO systems with one-bit ADCs

“…In practice, the complexity of Algorithms 2 and 3 is demanding because h(x) and ∇h(x) are required at each iteration, which are high-dimensional functions defined on C B where B ≫ M N . In recent works on channel estimation and data detection in the mmWave band [14], [15], [28], the FFT-based implementation is widely used because H can be approximated by (7) using overcomplete DFT matrices. In this paper, an FFT-based fast implementation of h(x) and ∇h(x) is proposed, which is motivated by [14], [15], [28].…”

“…To take the channel sparsity into account, iterative approximate MMSE estimators for mmWave massive MIMO systems with one-bit ADCs were proposed in [14], [15]. The generalized expectation consistent signal recovery (GEC-SR) algorithm in [14] is an iterative approximate MMSE estimator based on the turbo principle [16], which can be applied to any nonlinear function of the linearly-mapped signal to be estimated. Furthermore, the only constraint on the distribution of the signal to be estimated is that its elements must be independent and identically distributed (i.i.d.)…”