Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stop-band isolation. This TxL signal in combination with the second-order nonlinearity of the receive mixer may lead to a baseband (BB) second-order intermodulation distortion (IMD2) with twice the transmit signal bandwidth. In direct conversion receivers, this nonlinear IMD2 interference may cause a severe signal-to-interference-plus-noise ratio degradation of the wanted receive signal. This contribution presents a nonlinear Wiener model recursive-least-squares (RLS) type adaptive filter for the cancellation of the IMD2 interference in the digital BB. The included channel-select-, and DC-notch filter at the output of the proposed adaptive filter ensure that the provided IMD2 replica includes the receiver front-end filtering. A second, robust version of the nonlinear RLS algorithm is derived which provides numerical stability for highly correlated input signals which arise in e.g. LTE-A intra-band multi-cluster transmission scenarios. The performance of the proposed algorithms is evaluated by numerical simulations and by measurement data.

This article presents a comprehensive survey of the literature on self-interference (SI) in long-term evolution advanced (LTE-A) and fifth-generation (5G) new radio transceivers and should serve the reader as a guide and starting point for further work on SI management. Current trends in cellular transceiver designs are discussed, and reasons why new technologies, such as carrier aggregation, cause potential sensitivity degradation due to self-interfering signals are highlighted. The survey provides an overview of the most common interference mechanisms and continues with a taxonomy on SI mitigation architectures by comparing the strengths and weaknesses of various techniques.

The stochastic gradient descent (SGD) method is one of the most prominent first-order iterative optimisation algorithms, enabling linear adaptive filters as well as general nonlinear learning schemes. It is applicable to a wide range of objective functions, while featuring low computational costs for online operation. However, without a suitable step-size normalisation, the convergence and tracking behaviour of the stochastic gradient descent method might be degraded in practical applications. In this letter, a novel general normalisation approach is provided for the learning of (non-)holomorphic models with multiple independent parameter sets. The advantages of the proposed method are demonstrated by means of a specific widely-linear estimation example.

Direct-conversion transceivers are the predominating architecture in current mobile communication systems. Despite many advantages, this topology suffers from unavoidable mismatches in the analog part, which causes imbalance between the in-phase and quadrature (I/Q) component. In this paper, we present a novel fully digital, blind I/Q imbalance compensation algorithm that features extremely low computational complexity and high compensation performance for a wide range of input signal types. Different to many state-of-the-art compensation schemes, the approach is not based on a gradient descent optimization and does not require any global feedback. This simplifies the implementation at high data rates and reduces the configuration effort to a minimum. For comparison, we examine an existing method of moment-based estimator with similar properties, for which we also provide the detailed insights beyond available literature. For both algorithms, we provide a rigorous mathematical analysis, which is supported by simulations with a focus on various long-term evolution (LTE) signal types. In addition, hardware architectures, including field-programmable gate array (FPGA) verification, are presented for both algorithms.

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