One-Bit OFDM Receivers via Deep Learning

By Eren Balevi, Jeffrey G. Andrews. 2018

114 citations recorded by the source corpus.

This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization reduces greatly the complexity and power consumption but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms that have high peak-to-average power ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), we design a novel generative supervised deep neural network that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver—specifically, an autoencoder—jointly learns a precoder and decoder for data symbol detection. Since quantization prevents end-to-end training, we propose a two-step sequential training policy for this model. With synthetic data, our deep learning-based channel estimation can outperform least squares channel estimation for unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data detection, our proposed design achieves lower bit error rate (BER) in fading than unquantized OFDM at average SNRs up to 10 dB.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, methodology): “Toward this vision, at the physical layer, an E2E communication framework was studied under unknown channels using an autoencoder (AE) [19], [20], recurrent NN (RNN) [21], and generative adversarial network (GAN) [22].”
6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications (2020, background): “Examples of application of machine learning tools to the physical layer of a communication system have been already studied, under unknown channels, using an autoencoder (AE) [58], [59], a recurrent neural network (RNN) [60], and a generative adversarial network (GAN) [61].”