  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.
