arXiv subjects

Daniel Schäufele

Publications and source records attributed to Daniel Schäufele.

8 recordsLinked to original sources

arXiv2026

Uplink Multi-User MIMO Implementation in OpenAirInterface

Cell-Free Multiple-Input Multiple-Output (MIMO) and Open Radio Access Network (O-RAN) have been active research topics in the wireless communication community in recent years. As an open-source software implementation of the 3rd Generation Partnership Project (3GPP) 5th Generation (5G) protocol stack, OpenAirInterface (OAI) has become a valuable tool for deploying and testing new ideas in wireless communication systems. In this paper, we present our OAI-based real-time uplink Multi-User MIMO (MU-MIMO) testbed developed at Fraunhofer HHI. As a part of our Cell-Free MIMO testbed development, we built a 2x2 MU-MIMO system using general purpose computers and commercially available software defined radios (SDRs). Using a modified OAI next-Generation Node-B (gNB) and two unmodified OAI user equipment (UE), we show that it is feasible to use Sounding Reference Signal (SRS) channel estimates to compute uplink combiners. Our results verify that this method can be used to separate and decode signals from two users transmitting in non-orthogonal time-frequency resources. This work serves as an important verification step to build a complete Cell-Free MU-MIMO system that leverages time domain duplexing (TDD) reciprocity to perform downlink beamforming over multiple cells

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arXiv2025

Digital Post-Distortion Architectures for Nonlinear Power Amplifiers: Volterra and Kernel Methods

In modern 5G user equipments (UEs), the power amplifier (PA) contributes significantly to power consumption during uplink transmissions, especially in cell-edge scenarios. While reducing power backoff can enhance PA efficiency, it introduces nonlinear distortions that degrade signal quality. Existing solutions, such as digital pre-distortion, require complex feedback mechanisms for optimal performance, leading to increased UE complexity and power consumption. Instead, in this study we explore digital post-distortion (DPoD) techniques, which compensate for these distortions at the base station, leveraging its superior computational resources. In this study, we conduct an comprehensive study concerning the challenges and advantages associated with applying DPoD in time-domain, frequency-domain, and DFT-s-domain. Our findings suggest that implementing DPoD in the time-domain, complemented by frequency-domain channel equalization, strikes a good balance between low computational complexity and efficient nonlinearity compensation. In addition, we demonstrate that memory has to be taken into account regardless of the memory of the PA. Subsequently, we show how to pose the complex-valued problem of nonlinearity compensation in a real Hilbert space, emphasizing the potential performance enhancements as a result. We then discuss the traditional Volterra series and show an equivalent kernel method that can reduce algorithmic complexity. Simulations validate the results of our analysis and show that our proposed algorithm can significantly improve performance compared to state-of-the-art algorithms.

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arXiv2024

Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets

This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed information about the two captured datasets. iV2V covers sidelink communication scenarios between Automated Guided Vehicles (AGVs), while iV2I+ is conducted at an industrial setting where an autonomous cleaning robot is connected to a private cellular network. The combination of different communication technologies within a common measurement methodology provides insights that can be exploited by Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, prediction of quality of service or link selection. Moreover, the datasets are publicly available, labelled and prefiltered for fast on-boarding and applicability.

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arXiv2023

From Empirical Measurements to Augmented Data Rates: A Machine Learning Approach for MCS Adaptation in Sidelink Communication

Due to the lack of a feedback channel in the C-V2X sidelink, finding a suitable modulation and coding scheme (MCS) is a difficult task. However, recent use cases for vehicle-to-everything (V2X) communication with higher demands on data rate necessitate choosing the MCS adaptively. In this paper, we propose a machine learning approach to predict suitable MCS levels. Additionally, we propose the use of quantile prediction and evaluate it in combination with different algorithms for the task of predicting the MCS level with the highest achievable data rate. Thereby, we show significant improvements over conventional methods of choosing the MCS level. Using a machine learning approach, however, requires larger real-world data sets than are currently publicly available for research. For this reason, this paper presents a data set that was acquired in extensive drive tests, and that we make publicly available.

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arXiv2023

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

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arXiv2022

GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access

Non-orthogonal multiple access (NOMA) is an interesting technology that enables massive connectivity as required in future 5G and 6G networks. While purely linear processing already achieves good performance in NOMA systems, in certain scenarios, non-linear processing is mandatory to ensure acceptable performance. In this paper, we propose a neural network architecture that combines the advantages of both linear and non-linear processing. Its real-time detection performance is demonstrated by a highly efficient implementation on a graphics processing unit (GPU). Using real measurements in a laboratory environment, we show the superiority of our approach over conventional methods.

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arXiv2022

GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems

In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated platform. Partially linear multiuser detection, which combines the robustness of linear detection with the power of nonlinear methods, has been proposed for a massive connectivity scenario with the non-orthogonal multiple access (NOMA). This is a promising approach, but detecting payloads within a received orthogonal frequency division multiplexing (OFDM) radio frame requires the execution of a large number of inner product operations, which are the main computational burden of the algorithm. Although inner-product operations consist of simple kernel evaluations, their vast number poses a challenge in ultra-low latency (ULL) applications, because the time needed for computing the inner products might exceed the sub-millisecond latency requirement. To address this problem, this study demonstrates the acceleration of the inner-product operations through massive parallelization. The result is a GPU-accelerated real-time OFDM receiver that enables sub-millisecond latency detection to meet the requirements of 5th generation (5G) and beyond ultra-reliable and low latency communications (URLLC) systems. Moreover, the parallelization and acceleration techniques explored and demonstrated in this study can be extended to many other signal processing algorithms in Hilbert spaces, such as those based on projection onto convex sets (POCS) and adaptive projected subgradient method (APSM) algorithms. Experimental results and comparisons with the state-of-art confirm the effectiveness of our techniques.

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arXiv2019

Tensor Completion for Radio Map Reconstruction using Low Rank and Smoothness

Radio maps are important enablers for many applications in wireless networks, ranging from network planning and optimization to fingerprint based localization. Sampling the complete map is prohibitively expensive in practice, so methods for reconstructing the complete map from a subset of measurements are increasingly gaining attention in the literature. In this paper, we propose two algorithms for this purpose, which build on existing approaches that aim at minimizing the tensor rank while additionally enforcing smoothness of the radio map. Experimental results with synthetic measurements derived via ray tracing show that our algorithms outperform state of the art techniques.

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