Non-Linear Self-Interference Cancellation via Tensor Completion

Non-linear self-interference (SI) cancellation constitutes a fundamental problem in full-duplex communications, which is typically tackled using either polynomial models or neural networks. In this work, we explore the applicability of a recently proposed method based on low-rank tensor completion, called canonical system identification (CSID), to non-linear SI cancellation. Our results show that CSID is very effective in modeling and cancelling the non-linear SI signal and can have lower computational complexity than existing methods, albeit at the cost of increased memory requirements.Comment: To be 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

Nonlinear Digital Cancellation in Full-Duplex Devices using Spline-Based Hammerstein Model

In this paper, a novel digital self-interference canceller based on a Hammerstein adaptive filter is proposed and examined. The proposed system consists of a spline-interpolated lookup table to model the nonlinear power amplifier, followed by a linear filter accounting for the impulse response of the linear self-interference channel. The gradient descent based parameter learning algorithms are derived, which estimate the spline control points and the filter coefficients in a decoupled manner. The proposed digital canceller leads to a complexity reduction of 77% when compared to the existing state-of-the-art solutions. Performance evaluations using measured data from a complete inband full-duplex prototype system operating at 2.4 GHz ISM band show the effectiveness of the proposed technique, demonstrating that it obtains similar cancellation performance as the existing state-of-the-art canceller, regardless of its lower complexity. The measured digital self-interference cancellation values are 45 dB, 43 dB and 38 dB with 20 MHz, 40 MHz and 80 MHz channel bandwidths, respectively. These results indicate that the complexity-accuracy trade-off of the proposed decoupled spline-based cancellation approach is very favorable. Owing to the resulting decrease in the computational complexity, the proposed digital cancellation technique brings inband full-duplex transceivers one step closer to commercial deployments.acceptedVersionPeer reviewe