Benchmarking End-to-end Learning of MIMO Physical-Layer Communication

End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO transceivers, without any a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and multi-user MIMO and show that the gains of ML-based communication in the former two cases can be to a large extent ascribed to implicitly learned geometric shaping and bit and power allocation, not to learning new spatial encoders. For MU-MIMO, we demonstrate the feasibility of a novel method with centralized learning and decentralized executing, outperforming conventional zero-forcing. For each scenario, we provide explicit descriptions as well as open-source implementations of the selected neural-network architectures.Comment: 6 pages, 8 figures, conference pape

Learning Physical-Layer Communication with Quantized Feedback

Data-driven optimization of transmitters and receivers can reveal new modulation and detection schemes and enable physical-layer communication over unknown channels. Previous work has shown that practical implementations of this approach require a feedback signal from the receiver to the transmitter. In this paper, we study the impact of quantized feedback on data-driven learning of physical-layer communication. A novel quantization method is proposed, which exploits the specific properties of the feedback signal and is suitable for nonstationary signal distributions. The method is evaluated for linear and nonlinear channels. Simulation results show that feedback quantization does not appreciably affect the learning process and can lead to similar performance as compared to the case where unquantized feedback is used for training, even with 1-bit quantization. In addition, it is shown that learning is surprisingly robust to noisy feedback where random bit flips are applied to the quantization bits

End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairments

We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component. Our system achieves up to 60% SER reduction and up to 50% guard band reduction compared with the considered baseline scheme

Learning Optimal PAM Levels for VCSEL-based Optical Interconnects

An auto-encoder that optimizes a VCSEL-based fiber-optic system end-to-end and provides a 1.5dB sensitivity gain at higher temperatures is trained, utilizing a neural network that models the response of a VCSEL for a range of operating temperatures

Hydrophobicity Mechanism of Cementitious Material Containing Carboxylic Acid Ammonium

Hydrophobic treatment of cement pastes is most effective way to resist the penetration of water with aggressive ions to improve durability of cement-based materials. In this paper, the mechanism of carboxylic acid ammonium salt integral additionon hydrophobicity of cement-based materials is analyzed. The effects of carboxylic acid ammonium salt on hydrophobicity, moisture diffusion and hydration products are investigated by experimental methods.The results of water vapor sorption isotherm show that the addition of carboxylic acid ammonium salt increases the pore volume. The simplified analytical hydrodynamic model of cone type pore and Young’s relationship is proposed to analyze the relation between characteristic parameters of pore and the permeability of cementitious materials. It can be concluded that the hydrophobicity results from the change of pore structure and the hydrophobic surface of cone region. These results provide the guidance to design the durability and develop the new hydrophobic agent used in cementitious material

Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning

We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between downsampled messages at the receiver side. The algorithm does not require any knowledge about the underlying hardware or channel. For a generalized memory polynomial power amplifier and additive white Gaussian noise channel, we show that the proposed algorithm achieves performance improvements in terms of symbol error rate compared with an indirect learning architecture even when the latter is coupled with a full sampling rate ADC in the feedback path. Furthermore, it maintains a satisfactory adjacent channel power ratio

Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning

We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between downsampled messages at the receiver side. The algorithm does not require any knowledge about the underlying hardware or channel. For a generalized memory polynomial power amplifier and additive white Gaussian noise channel, we show that the proposed algorithm achieves performance improvements in terms of symbol error rate compared with an indirect learning architecture even when the latter is coupled with a full sampling rate ADC in the feedback path. Furthermore, it maintains a satisfactory adjacent channel power ratio

Over-the-fiber Digital Predistortion Using Reinforcement Learning

We demonstrate, for the first time, experimental over-the-fiber training of transmitter neural networks (NNs) using reinforcement learning. Optical back-to-back training of a novel NN-based digital predistorter outperforms arcsine-based predistortion with up to 60\% bit-error-rate reduction