Reliable Low Resolution 01-4DM Receivers via Deep Learning

Date

2018-01-01

Author

Balevi, Eren
Andrews, Jeffrey G.

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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 greatly reduces complexity and power consumption, but makes accurate data detection difficult. This is particularly true for multicarrier waveforms, which have high peak-to-average ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-hit quantization typically results in an error floor even at moderately low signal-to-noise ratio (SNR) such as 5 dB. 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. Our proposed design achieves lower bit error rate (BER) in fading than unquantized (full-resolution) OFDM at average SNRs up to 10 dB.

Subject Keywords

Deep learning, OFDM data detection, one-bit quantization, CHANNEL ESTIMATION, MIMO SYSTEMS, DETECTOR

URI

https://hdl.handle.net/11511/100646

Conference Name

52nd Asilomar Conference on Signals, Systems, and Computers

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. Balevi and J. G. Andrews, “Reliable Low Resolution 01-4DM Receivers via Deep Learning,” presented at the 52nd Asilomar Conference on Signals, Systems, and Computers, California, Amerika Birleşik Devletleri, 2018, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100646.