Spline-Based Adaptive Cancellation of Even-Order Intermodulation Distortions in LTE-A/5G RF Transceivers

TL;DR

This paper introduces two novel spline-based adaptive algorithms for digital self-interference cancellation in RF transceivers, effectively mitigating intermodulation distortions in LTE-A/5G systems and outperforming existing methods.

Contribution

The paper presents complex-valued spline-based Wiener models with stochastic gradient descent optimization, enabling efficient and hardware-friendly cancellation of even-order intermodulation distortions.

Findings

Outperforms state-of-the-art LMS variants in realistic scenarios

Achieves performance comparable to kernel RLS with fewer operations

Supports complex-valued signals and hardware implementation features

Abstract

Radio frequency transceivers operating in in-band full-duplex or frequency-division duplex mode experience strong transmitter leakage. Combined with receiver nonlinearities, this causes intermodulation products in the baseband, possibly with higher power than the desired receive signal. In order to restore the receiver signal-to-noise ratio in such scenarios, we propose two novel digital self-interference cancellation approaches based on spline interpolation. Both employ a Wiener structure, thereby matching the baseband model of the intermodulation effect. Unlike most state-of-the-art spline-based adaptive learning schemes, the proposed concept allows for complex-valued in- and out-put signals. The optimization of the model parameters is based on the stochastic gradient descent concept, where the convergence is supported by an appropriate step-size normalization. Additionally, we provide a gain control scheme and enable pipelining in order to facilitate a hardware implementation. An optional input transform improves the performance consistency for correlated sequences. In a realistic interference scenario, the proposed algorithms clearly outperform a state-of-the-art least mean squares variant with comparable complexity, which is specifically tailored to second-order intermodulation distortions. The high flexibility of the spline interpolation allows the spline-based Wiener models to match the performance of the kernel recursive least squares algorithm at less than 0.6% of the arithmetic operations.