Matias Turunen’s research while affiliated with Tampere University and other places

This article proposes a nonlinear known-interference cancellation (KIC) algorithm that allows a receiver to suppress the interference from a high-power cooperative jammer given that the transmitted interference is known in advance to the receiver. The proposed algorithm achieves this by estimating and compensating for the nonlinear power amplifier (PA) distortions, wireless channel effects, and frequency offsets that alter the transmitted interference as it propagates to the receiver. Measurements with commercial off-the-shelf radio platforms in both laboratory and outdoor conditions are presented. Their results demonstrate that the proposed method is able to cancel known interference (KI) with moderate residuals for a wide range of received signal-to-interference-plus-noise ratios (SINRs). This facilitates processing a signal of interest that is otherwise masked by the interference signal.

In this letter, we propose a novel self-interference (SI) cancellation solution for asymmetric in-band full-duplex (IBFD) systems, where the downlink (DL) signals are noncontiguously aggregated within the available band. The method builds on a Hammerstein model with parallel spline-interpolated 2-D lookup tables (LUTs), together with efficient gradient-descent parameter learning rules. The provided RF measurement results harnessing a GaN Doherty power amplifier (PA) at 3.5 GHz demonstrate digital SI cancellation levels up to 35 dB, with substantially reduced processing complexity compared with the reference polynomial model. These methods and results pave the way toward enhanced duplexing capabilities and flexibility in the emerging 6G era.

In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on end-to-end processing solutions, from raw I/Q samples, via channel parameter estimation to user equipment (UE) and landmark location information in millimeter-wave (mmWave) networks, with minimal prior knowledge. Firstly, we propose a new multipath channel parameter estimation solution that operates directly with beam reference signal received power (BRSRP) measurements, alleviating the need to know the true antenna beam-patterns or the underlying beamforming weights. Additionally, the method has built-in robustness against unavoidable antenna sidelobes. Secondly, we propose new snapshot SLAM algorithms that have increased robustness and identifiability compared to prior art, in practical built environments with complex clutter and multi-bounce propagation scenarios, and do not rely on any a priori motion model. The performance of the proposed methods is assessed at the 60GHz mmWave band, via both realistic ray-tracing evaluations as well as true experimental measurements, in an indoor environment. A wide set of offered results demonstrate the improved performance, compared to the relevant prior art, in terms of the channel parameter estimation as well as the end-to-end SLAM performance. Finally, the article provides the measured 60GHz data openly available for the research community, facilitating results reproducibility as well as further algorithm development.

In this article, we present a highly accurate recurrent neural network (RNN) for behavioral modeling and digital predistortion (DPD) of radio frequency (RF) power amplifiers (PAs). We describe a deep, residual recurrent unit (RRU) that minimizes the overhead of the recurrent operation. Phase normalization is incorporated with the proposed unit to allow for efficient processing of the baseband signal phase with the real-valued RNN structure. Furthermore, we augment the phase normalization concept with dedicated envelope cell states that support the mapping of RF envelope dominated distortions. Combination with a trainable, input-ended finite impulse response (FIR) filtering leads us to proposing the augmented phase-normalized RRU (APNRRU). Our experimental validation, including a detailed modeling study of the proposed concepts with three different GaN Doherty PA units, as well as several DPD linearization examples, shows that the APNRRU offers excellent linearization already with modest complexity of just 550 model parameters. In addition, the results demonstrate the ability to linearize also demanding wideband PA operation with noncontiguous multicarrier signals with 400-MHz composite bandwidth, outperforming the prior art solutions.

This work showcases the design, implementation, and verification of a vector antenna (VA) capable of direction finding. By adopting the tapered slot (also known as Vivaldi) element as the constitutive VA element and a four-season layout, the VA in this work can identify the radio source in a wide spectrum range in the three-dimensional space. Practical issues, such as steering vector calibration, exploitation of polarization, and angle estimation with partial electromagnetic measurements, are addressed to ensure the functionality of the proposed VA in practice. A four-channel phase-locked software-defined radio platform and quasi-real-time angle estimation algorithms are designed and implemented to verify the direction-finding (DF) capability of the VA for directly propagated, reflected, and diffracted radio signals, respectively. The results of the experiment prove the effectiveness of the theory and our practical VA measures for 3D direction finding under various conditions and potentially inspire localization, mapping, and navigation in short-range scenarios.

Physical layer security is a sought-after concept to complement the established upper layer security techniques in wireless communications. An appealing approach to achieve physical layer security is to use cooperative jamming with interference that is known to and suppressible by the legitimate receiver but unknown to, and hence not suppressible by, the eavesdropper. Suppressing known interference (KI), however, is challenging due to the numerous unknowns, including carrier and sampling frequency offsets, that impact its reception. This letter presents a measurement campaign that captures this challenge and then demonstrates the feasibility of solving that challenge by cancelling the KI using the frequency offsets least mean squares (FO-LMS) algorithm. Results show that KI suppression directly improves processing the signal-of-interest and that cooperative jamming effectively provides security at the physical layer.