  The use of low-resolution data converters in the radio-frequency (RF) chains
of all-digital massive multiple-input multiple-output (MIMO) basestations
promises significant reductions in power consumption, hardware costs, and
interconnect bandwidth. We propose a quantization-aware data-detection
algorithm which mitigates the performance loss of 1-bit quantized massive MIMO
orthogonal frequency-division multiplexing (OFDM) systems. Since the system
performance heavily depends on the quality of channel estimates, we also
develop a nonlinear 1-bit channel estimation algorithm that builds upon the
proposed data detection algorithm. We show that the proposed algorithms
significantly outperform linear data detectors and channel estimators in terms
of bit error rate. For the proposed nonlinear data detection algorithm, we
develop a very large scale integration (VLSI) architecture and present
implementation results on a Xilinx Virtex-7 field programmable gate array
(FPGA). Our implementation results are, to the best of our knowledge, the first
for 1-bit massive MU-MIMO-OFDM systems and demonstrate comparable hardware
efficiency with respect to state-of-the-art linear data detectors designed for
systems with high-resolution data converters, while achieving lower bit error
rate.
