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.

32 pages, 12 figures; accepted by IEEE JSAC special issue on millimeter wave communications for future mobile networks

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• Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity
• MIMO Detection for Reconfigurable Intelligent Surface-Assisted Millimeter Wave Systems
• Estimation for High-Dimensional Multi-Layer Generalized Linear Model -- Part I: The Exact MMSE Estimator
• Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach
• Time-Varying Downlink Channel Tracking for Quantized Massive MIMO Networks
• Soft-Output Detection Methods for Sparse Millimeter Wave MIMO Systems with Low-Precision ADCs
• Performance Analysis of Multi-Cell Millimeter Wave Massive MIMO Networks with Low-Precision ADCs