Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks

Full-duplex systems require very strong self-interference cancellation in order to operate correctly and a significant part of the self-interference signal is due to non-linear effects created by various transceiver impairments. As such, linear cancellation alone is usually not sufficient and sophisticated non-linear cancellation algorithms have been proposed in the literature. In this work, we investigate the use of a neural network as an alternative to the traditional non-linear cancellation method that is based on polynomial basis functions. Measurement results from a full-duplex testbed demonstrate that a small and simple feed-forward neural network canceler works exceptionally well, as it can match the performance of the polynomial non-linear canceler with significantly lower computational complexity.Comment: Presented at the IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 201

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

Reference Receiver Based Digital Self-Interference Cancellation in MIMO Full-Duplex Transceivers

In this paper we propose and analyze a novel self-interference cancellation structure for in-band MIMO full-duplex transceivers. The proposed structure utilizes reference receiver chains to obtain reference signals for digital self-interference cancellation, which means that all the transmitter-induced nonidealities will be included in the digital cancellation signal. To the best of our knowledge, this type of a structure has not been discussed before in the context of full-duplex transceivers. First, we will analyze the overall achievable performance of the proposed cancellation scheme, while also providing some insight into the possible bottlenecks. We also provide a detailed formulation of the actual cancellation procedure, and perform an analysis into the effect of the received signal of interest on self-interference coupling channel estimation. The achieved performance of the proposed reference receiver based digital cancellation procedure is then assessed and verified with full waveform simulations. The analysis and waveform simulation results show that under practical transmitter RF/analog impairment levels, the proposed reference receiver based cancellation architecture can provide substantially better self-interference suppression than any existing solution, despite deploying only low-complexity linear digital processing.Comment: 7 pages, 4 figures. To be presented in the 2014 IEEE Broadband Wireless Access Worksho

Feasibility of In-band Full-Duplex Radio Transceivers with Imperfect RF Components: Analysis and Enhanced Cancellation Algorithms

In this paper we provide an overview regarding the feasibility of in-band full-duplex transceivers under imperfect RF components. We utilize results and findings from the recent research on full-duplex communications, while introducing also transmitter-induced thermal noise into the analysis. This means that the model of the RF impairments used in this paper is the most comprehensive thus far. By assuming realistic parameter values for the different transceiver components, it is shown that IQ imaging and transmitter-induced nonlinearities are the most significant sources of distortion in in-band full-duplex transceivers, in addition to linear self-interference. Motivated by this, we propose a novel augmented nonlinear digital self-interference canceller that is able to model and hence suppress all the essential transmitter imperfections jointly. This is also verified and demonstrated by extensive waveform simulations.Comment: 7 pages, presented in the CROWNCOM 2014 conferenc