Reliable OFDM Receiver with Ultra-Low Resolution ADC

Abstract

The use of low-resolution analog-to-digital converters (ADCs) can significantly reduce power consumption and hardware cost. However, their resulting severe nonlinear distortion makes reliable data transmission challenging. For orthogonal frequency division multiplexing (OFDM) transmission, the orthogonality among subcarriers is destroyed. This invalidates conventional OFDM receivers relying heavily on this orthogonality. In this study, we move on to quantized OFDM (Q-OFDM) prototyping implementation based on our previous achievement in optimal Q-OFDM detection. First, we propose a novel Q-OFDM channel estimator by extending the generalized Turbo (GTurbo) framework formerly applied for optimal detection. Specifically, we integrate a type of robust linear OFDM channel estimator into the original GTurbo framework and derive its corresponding extrinsic information to guarantee its convergence. We also propose feasible schemes for automatic gain control, noise power estimation, and synchronization. Combined with the proposed inference algorithms, we develop an efficient Q-OFDM receiver architecture. Furthermore, we construct a proof-of-concept prototyping system and conduct over-the-air (OTA) experiments to examine its feasibility and reliability. This is the first work that focuses on both algorithm design and system implementation in the field of low-resolution quantization communication. The results of the numerical simulation and OTA experiment demonstrate that reliable data transmission can be achieved.

14 pages, 17 figures; accepted by IEEE Transactions on Communications

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• Massive MIMO with 1-bit ADC
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Cited by in corpus (4)

• On Minimizing Symbol Error Rate Over Fading Channels with Low-Resolution Quantization
• Dynamic Metasurface Antennas for MIMO-OFDM Receivers with Bit-Limited ADCs
• Implementation and Performance Analysis of a Low Resolution OFDM System Prototype With Low Cost Hardware
• On the Capacity-Achieving Input of the Gaussian Channel with Polar Quantization