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

This article investigates novel adaptive self-interference cancellation solutions and the total integrated cancellation performance of a mobile single-antenna inband full-duplex transceiver. First, novel self-adaptive digital self-interference cancellation algorithms are described, with an emphasis on tracking of time-varying self-interference coupling channel in a mobile device as well as on structural ability to suppress also nonlinear self-interference with highly nonlinear mobile power amplifiers. This leads to an advanced self-adaptive nonlinear digital canceller which utilizes a novel orthogonalization procedure for nonlinear basis functions, together with low-cost LMS-based parameter learning. The achievable self-interference cancellation performance is then evaluated with actual RF measurements using mobile device scale RF components, in particular a highly nonlinear PA. The measurements also incorporate a novel self-adaptive RF cancellation circuit in order to realistically assess the total integrated cancellation performance. The reported results show that highly efficient self-interference cancellation can be achieved also in a mobile device, despite a heavily nonlinear PA and limited computing and hardware resources. The proposed cancellation solutions, when integrated together, show that 100 dB of self-interference can be cancelled using a 20 MHz LTE waveform, while the SI can be attenuated by over 110 dB with a narrower bandwidth of 1.4 MHz, all measured at 2.4 GHz ISM band. Furthermore, these results are achieved using a highly nonlinear transmitter power amplifier and fully adaptive canceller structures which can track a rapidly changing coupling channel in a mobile full-duplex device.

Nonlinear self-interference (SI) cancellation is essential for mitigating the impact of transmitter-side nonlinearity on overall SI cancellation performance in flexible duplex systems, including in-band full-duplex (IBFD) and sub-band full-duplex (SBFD). Digital SI cancellation (SIC) must address the nonlinearity in the power amplifier (PA) and the in-phase/quadrature-phase (IQ) imbalance from up/down converters at the base station (BS), in addition to analog SIC. In environments with rich signal reflection paths, however, the required number of delayed taps for time-domain nonlinear SI cancellation increases exponentially with the number of multipaths, leading to excessive complexity. This paper introduces a novel, low-complexity, frequency domain nonlinear SIC, suitable for flexible duplex systems with multiple-input and multiple-output (MIMO) configurations. The key approach involves decomposing nonlinear SI into a nonlinear basis and categorizing them based on their effectiveness across any flexible duplex setting. The proposed algorithm is founded on our analytical results of intermodulation distortion (IMD) in the frequency domain and utilizes a specialized pilot sequence. This algorithm is directly applicable to orthogonal frequency division multiplexing (OFDM) multi-carrier systems and offers lower complexity than conventional digital SIC methods. Additionally, we assess the impact of the proposed SIC on flexible duplex systems through system-level simulation (SLS) using 3D ray-tracing and proof-of-concept (PoC) measurement.

This paper addresses the modeling and digital cancellation of self-interference in in-band full-duplex (FD) transceivers with multiple transmit and receive antennas. The self-interference modeling and the proposed nonlinear spatio-temporal digital canceller structure takes into account, by design, the effects of I/Q modulator imbalances and power amplifier (PA) nonlinearities with memory, in addition to the multipath self-interference propagation channels and the analog RF cancellation stage. The proposed solution is the first cancellation technique in the literature which can handle such a self-interference scenario. It is shown by comprehensive simulations with realistic RF component parameters and with two different PA models to clearly outperform the current state-of-the-art digital self-interference cancellers, and to clearly extend the usable transmit power range.

Genuine full-duplex operation requires effective mitigation of self-interference (SI) due to simultaneous transmission and reception at the same frequency band. In addition to its well-known harmful effect on signals of high peak-to-average power ratio, nonlinear behavior of a power amplifier (PA) complicates SI cancellation and induces spectral regrowth. We introduce a digital predistortion architecture that linearizes the response of the PA in a full-duplex transceiver. Specifically, we propose a two-step procedure to estimate the predistorter parameters and the SI canceller coefficients. Different from a direct application of conventional predistorters, the proposed architecture does not need an extra RF chain to estimate the PA response and exploits the inherent SI signal instead. Finally, simulation results show that the proposed scheme is able to increase significantly the signal-to-interference-plus-noise ratio at the transceiver output and to reduce out-of-band emissions when compared to linear and nonlinear cancellation without predistortion.

In this paper, a novel non-linear digital self- interference cancellation algorithm is proposed for full-duplex wireless systems. The proposed algorithm constructs a replica of the nonlinearly-distorted transmitted signal using Itô generalization of the Hermite (Itô-Hermite) polynomials. Because Itô-Hermite polynomials constitute orthogonal basis functions of any non-linear function when the input is a complex Gaussian random process, the proposed least mean squares (LMS) adaptive filtering based on Itô-Hermite polynomials optimally cancels non-linearly distorted self-interference signals in full-duplex systems. From numerical results, it is shown that the proposed algorithm provides noticeable gains in terms of both a fast convergence rate and the low computational complexity for the self-interference cancellation compared to the existing LMS algorithm using Volterra- series as basis functions with basis function pre- orthogonalization.

In-band full-duplex (FD) technology has emerged as one of the most promising solutions for spectrum scarcity, allowing users to transmit and receive simultaneously at the same center frequency. However, nonlinear self-interference (SI) introduced by nonideal transceiver components is considered as the main challenge in FD systems. Traditionally, nonlinear SI is characterized by polynomial models, which are computationally expensive. In this letter, a novel neural network (NN) structure, refered to as the convolutional NN with Mish activation function based on knowledge distillation (KD-MCNN), as well as three practical methods for NN design and training are proposed to model nonlinear SI with lower computational complexity. To accommodate hardware with varying computational capabilities, nine KD-MCNN models with different scales are provided. The numerical simulations show that KD-MCNN cancellers exhibit superior performance and computational efficiency compared to both polynomial-based cancellers and existing NN-based cancellers. In particular, the nonlinear SI power can be further reduced by approximately 7.70 dB via the KD-MCNN teacher canceller, which is only 2.48 dB above the receiver's noise floor.

For the self-interference problem in full-duplex wireless communication systems, a nonlinear self-interference cancellation method based on the CNN-BiLSTM-Attention network is proposed. The method first captures the spatial features of the input sequence through the CNN layer, then extracts the temporally-related features using a BiLSTM layer with multiple LSTM units that are bidirectionally connected. The Attention layer is used to extract the global dependencies within the time series data, allowing the network to dynamically adjust the weights of the individual time steps during learning. Finally, a fully connected layer is employed to construct the nonlinear self-interference signal, which is then removed at the receiver to achieve the purpose of self-interference suppression. Simulation results show that the algorithm exhibits excellent performance in nonlinear self-interference cancellation.

Requiring a single frequency band for transmission, full-duplex (FD) systems can ideally double the spectral efficiency. However, in addition to the information signal, radiated from user's own transmit antenna generates a strong self-interference (SI). To eliminate effects of SI, active and passive cancellation techniques are used in the literature. However, omitted in these studies is the fact that the time dispersion in the SI channel may cause an error floor. In this paper, it is verified that the SI channel is time dispersive via measurements. Later, we present an analysis in order to determine the error performance. Following these results, a fractionally spaced SI canceler is proposed to eliminate the error floor. We verify our analytical results via simulations, showing that the proposed SI canceler can critically enhance the performance of FD systems.

In this paper, we propose and demonstrate a low-complexity fiber nonlinearity impairments compensation (NLC) scheme based on simple recurrent neural network (SRNN), combining with the triplets derived from the first-order perturbation solution of the coupled nonlinear Schrodinger equation. Benefitting from the time memory of SRNN, the proposed NLC can reduce the computational complexity without sacrificing the compensation performance compared with the schemes based on memoryless feedforward neural networks. To verify the feasibility of the proposed NLC, the simulation systems of single channel and three channel 30 GBaud polarization division multiplexing (PDM) 16 quadrature amplitude modulation (QAM) over 2400 km standard single mode fiber transmission are constructed respectively. Comprehensive numerical simulation results demonstrate that although the NLC performance of proposed scheme is inferior to that of digital back propagation (DBP) with much higher complexity and impracticality, it can still achieve the competitive NLC performance with lower computational complexity compared to the schemes based on deep neural network (DNN) and artificial neural network (ANN). Moreover, it is further proved that using triplets as the feature required for NLC can cause the pattern of pseudo random bit sequence to be destroyed, resulting in the neural network-based NLC scheme not suffering from the problem of overestimation for performance. Additionally, the time memory of SRNN enables the number of triplets in training set required by the SRNN-based NLC to be reduced by 169% compared to that of DNN and ANN. The experimental results of 16 GBaud PDM-16QAM wavelength division multiplexing coherent systems demonstrate that compared to with ANN and DNN, the comparable NLC performance can also be achieved by the proposed scheme with lower complexity.

Nonlinear interference cancellation multiuser detection algorithms have less BER and are less complex than linear multiuser detection methods. We present a hybrid switched mode detector that uses existing interference cancellation methods i.e. Parallel Interference Cancellation (PIC) and Successive Interference cancellation (SIC) based on channel conditions. This proposed detector takes decisions based on number of users on the channel and switches either to SIC method or PIC method. The switching mechanism is exploited by performance complexity tradeoff between matched filter detector and nonlinear interference cancellation methods.

Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.

House price forecasting with neural networks

An efficient method for simultaneous nonlinear self-interference (SI) cancellation and signal-of-interest (SOI) recovery is proposed and experimentally verified in a mmWave over fiber testbed, based on a specially designed dual-input deep neural network (DI-DNN). The mmWave band has been adopted in the U.S. to provide 5G wireless access services due to its wide-bandwidth, light-of-sight propagation, and inherent compatibility with small cell architecture. To further meet the uprising bandwidth-demanding services, the in-band full-duplex (IBFD) scheme is introduced to magnify the mmWave channel capacity for the 5G and beyond wireless communications. Fortunately, the mmWave band is more realistic to implement IBFD compared with the conventional sub-6GHz band, as it operates as a highly directional beam and shorter transmission distance resulting in a higher SOI to SI ratio. In our proposed solution, a DI-DNN is, for the first time, proposed and implemented to simultaneously realize the SI cancellation and the SOI recovery. Moreover, we firstly mitigate the nonlinearity arising from the transmitters of both the SI and SOI, as well as the nonlinear crosstalk from the SI to the SOI after detection by a nonlinear receiver. The demonstration of the DI-DNN joint with the experimental implementation in a mmWave over fiber system, promise spectral efficient, high-capacity signal transport in the 5G and beyond wireless communications.

In wireless full-duplex communication systems, selfinterference (SI) cancellation is a key technique to ensure simultaneous transmission and reception on the same carrier frequency. During the SI cancellation, it has been shown that the oscillator phase noise dramatically affects the cancellation capability and thus needs consideration and suppression. In this paper, a novel time-domain digital SI cancellation approach is proposed to cancel the transmitter and receiver phase noises along with the incoming SI signal, without estimating either the SI channel response or the phase noise associated coefficients. For proofof-concept evaluation, simulations are performed on orthogonal frequency division multiplexing (OFDM)-modulated signals of 20 MHz bandwidth with 16 QAM constellations to verify the effectiveness of the proposed method. It is demonstrated that the proposed approach provides at least 3 dB improvement on the SI cancellation capability and at least 5 dB increase on the interference tolerance over the conventional cancellation approach that uses the frequency domain SI channel estimation.

This paper reports results with ABI, a portable noninvasive brain-computer interface. It uses 8 scalp electrodes to measure spontaneous EEG signals from which we extract simple power spectral features. The features are fed to a simple local neural network that recognizes reliably 3 different mental states. We compare the performance of this local classifier to more complex time-processing neural networks. We also illustrate the control of a complex brain-actuated device; i.e., a robot moving along smooth and safe paths between rooms.