Identification of Non-Linear RF Systems Using Backpropagation

In this work, we use deep unfolding to view cascaded non-linear RF systems as model-based neural networks. This view enables the direct use of a wide range of neural network tools and optimizers to efficiently identify such cascaded models. We demonstrate the effectiveness of this approach through the example of digital self-interference cancellation in full-duplex communications where an IQ imbalance model and a non-linear PA model are cascaded in series. For a self-interference cancellation performance of approximately 44.5 dB, the number of model parameters can be reduced by 74% and the number of operations per sample can be reduced by 79% compared to an expanded linear-in-parameters polynomial model.Comment: To be presented at the 2020 IEEE International Conference on Communications (Workshop on Full-Duplex Communications for Future Wireless Networks

On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receiver, which requires the use of cancellation techniques. A wide range of techniques for analog and digital self-interference cancellation have already been presented in the literature. However, their evaluation focuses on cases where the underlying physical parameters of the full-duplex system do not vary significantly. In this paper, we focus on adaptive digital cancellation, motivated by the fact that physical systems change over time. We examine some of the different cancellation methods in terms of their performance and implementation complexity, considering the cost of both cancellation and training. We then present a comparative analysis of all these methods to determine which perform better under different system performance requirements. We demonstrate that with a neural network approach, the reduction in arithmetic complexity for the same cancellation performance relative to a state-of-the-art polynomial model is several orders of magnitude.Comment: Presented at the 2020 Asilomar Conference for Signals, Systems, and Computer

Hardware Implementation of Neural Self-Interference Cancellation

In-band full-duplex systems can transmit and receive information simultaneously on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we describe a hardware architecture for a neural network-based non-linear self-interference (SI) canceller and we compare it with our own hardware implementation of a conventional polynomial based SI canceller. In particular, we present implementation results for a shallow and a deep neural network SI canceller as well as for a polynomial SI canceller. Our results show that the deep neural network canceller achieves a hardware efficiency of up to $312.8$ Msamples/s/mm$^2$ and an energy efficiency of up to $0.9$ nJ/sample, which is $2.1\times$ and $2\times$ better than the polynomial SI canceller, respectively. These results show that NN-based methods applied to communications are not only useful from a performance perspective, but can also be a very effective means to reduce the implementation complexity.Comment: Accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and System

Identification of Non-Linear RF Systems Using Backpropagation

In this work, we use deep unfolding to view cascaded non-linear RF systems as model-based neural networks. This view enables the direct use of a wide range of neural network tools and optimizers to efficiently identify such cascaded models. We demonstrate the effectiveness of this approach through the example of digital self-interference cancellation in full-duplex communications where an IQ imbalance model and a non-linear PA model are cascaded in series. For a self-interference cancellation performance of approximately 44.5 dB, the number of model parameters can be reduced by 74% and the number of operations per sample can be reduced by 79% compared to an expanded linear-in-parameters polynomial model

Advanced Machine Learning Techniques for Self-Interference Cancellation in Full-Duplex Radios

In-band full-duplex systems allow for more efficient use of temporal and spectral resources by transmitting and receiving information at the same time and on the same frequency. However, this creates a strong self-interference signal at the receiver, making the use of self-interference cancellation critical. Recently, neural networks have been used to perform digital self-interference with lower computational complexity compared to a traditional polynomial model. In this paper, we examine the use of advanced neural networks, such as recurrent and complex-valued neural networks, and we perform an in-depth network architecture exploration. Our neural network architecture exploration reveals that complex-valued neural networks can significantly reduce both the number of floating-point operations and parameters compared to a polynomial model, whereas the real-valued networks only reduce the number of floating-point operations. For example, at a digital self-interference cancellation of 44.51 dB, a complex-valued neural network requires 33.7 % fewer floating-point operations and 26.9 % fewer parameters compared to the polynomial model.Comment: Presented at the 2019 Asilomar Conference for Signals, Systems, and Computer

ComplexBeat: Breathing Rate Estimation from Complex CSI

In this paper, we explore the use of channel state information (CSI) from a WiFi system to estimate the breathing rate of a person in a room. In order to extract WiFi CSI components that are sensitive to breathing, we propose to consider the delay domain channel impulse response (CIR), while most state-of-the-art methods consider its frequency domain representation. One obstacle while processing the CSI data is that its amplitude and phase are highly distorted by measurement uncertainties. We thus also propose an amplitude calibration method and a phase offset calibration method for CSI measured in orthogonal frequency-division multiplexing (OFDM) multiple- input multiple-output (MIMO) systems. Finally, we implement a complete breathing rate estimation system in order to showcase the effectiveness of our proposed calibration and CSI extraction methods

Hardware Implementation of Neural Self-Interference Cancellation

In-band full-duplex systems can transmit and receive information simultaneously and on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we describe a hardware architecture for a neural network-based non-linear self-interference (SI) canceller and we compare it with our own hardware implementation of a conventional polynomial based SI canceller. Our results show that, for the same SI cancellation performance, the neural network canceller has an 8.1x smaller area and requires 7.7x less power than the polynomial canceller. Moreover, the neural network canceller can achieve 7 dB more SI cancellation while still being 1.2x smaller than the polynomial canceller and only requiring 1.3x more power. These results show that NN-based methods applied to communications are not only useful from a performance perspective, but can also lead to order-of-magnitude implementation complexity reductions