Title: Bayesian Optimal Data Detector for mmWave OFDM System with Low-Resolution ADC,
Abstract: Orthogonal frequency division multiplexing (OFDM) has been widely used in
communication systems operating in the millimeter wave (mmWave) band to combat
frequency-selective fading and achieve multi-Gbps transmissions, such as IEEE
802.15.3c and IEEE 802.11ad. For mmWave systems with ultra high sampling rate
requirements, the use of low-resolution analog-to-digital converters (ADCs)
(i.e., 1-3 bits) ensures an acceptable level of power consumption and system
costs. However, orthogonality among sub-channels in the OFDM system cannot be
maintained because of the severe non-linearity caused by low-resolution ADC,
which renders the design of data detector challenging. In this study, we
develop an efficient algorithm for optimal data detection in the mmWave OFDM
system with low-resolution ADCs. The analytical performance of the proposed
detector is derived and verified to achieve the fundamental limit of the
Bayesian optimal design. On the basis of the derived analytical expression, we
further propose a power allocation (PA) scheme that seeks to minimize the
average symbol error rate. In addition to the optimal data detector, we also
develop a feasible channel estimation method, which can provide high-quality
channel state information without significant pilot overhead. Simulation
results confirm the accuracy of our analysis and illustrate that the
performance of the proposed detector in conjunction with the proposed PA scheme
is close to the optimal performance of the OFDM system with infinite-resolution
ADC.