Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

Abstract

In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifically, three novel deep learning based convolutional neural network receivers are devised, containing layers in time-and/or frequency-domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Applicable training procedures are also described, such that the learned layers in the receiver processing properly generalize over different nonlinear distortion and multipath channel characteristics. Extensive set of numerical results is provided, in the context of 5G NR uplink (UL) incorporating also measured terminal power amplifier (PA) characteristics. The obtained results show that the proposed receiver systems are able to clearly outperform the classical linear minimum meansquared error (LMMSE) receiver as well as the existing ML receiver approaches, especially when the EVM is high compared to modulation order. This is particularly so when the devised ML receiver is of hybrid nature with layers both in time and frequency. The proposed ML receivers can thus facilitate pushing the terminal PA systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage. Through combining the obtained radio link performance results with link budget calculations, all carried out at the 28 GHz mmWave band, it is shown that the proposed ML receivers can enhance the network coverage in terms of maximum UL link distances by close to 100%, when compared to classical LMMSE receiver based networks.

Novel Approach towards a Fully Deep Learning-Based IoT Receiver Architecture: From Estimation to Decoding

“…Although each model mentioned has shown promise for different portions of an OFDM receiver, other approaches implement deep learning, utilizing multiple separate models to progress toward a completely ML-based receiver. The authors of [8] propose different time domain and frequency domain models to improve an OFDM receiver's response to increased Error Vector Magnitude while still utilizing traditional receiver components.…”

Digital Post-Distortion for Multi-Layer MIMO-OFDM

“…where θ (i) m,d denote the ML-DPoI processing coefficients. Similar to (9), and to support efficient parameter estimation, it is convenient to represent (12) in vector-matrix notation. Accounting for all involved N L parallel layers, we express this as…”

“…The seminal model-based works in [3], [4] introduce the socalled PA nonlinearity cancellation (PANC) and reconstruction of distorted signals (RODS) methods, respectively, in ordinary single-antenna or single-stream system context. Further singlestream refinements are provided, e.g., in [7], [8], while [9] develops machine learning (ML) based physical-layer receiver for single-stream OFDM transmission showing robustness against PA distortion. Additionally, different multiantenna or MIMO variants have been considered in [10]- [17].…”