arXiv subjects

Sławomir Stańczak

Publications and source records attributed to Sławomir Stańczak.

At least 19 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

CISSIR: Beam Codebooks with Self-Interference Reduction Guarantees for Integrated Sensing and Communication Beyond 5G

We propose a beam codebook design for integrated sensing and communication (ISAC) that reduces self-interference (SI) to alleviate analog distortion. Our optimization framework, which considers either tapered beamforming or phased arrays for both analog and hybrid schemes, modifies given reference codebooks such that a certain SI power level is achieved. In contrast to other low-SI codebooks, which often rely on hardly interpretable optimization parameters, we provide design guidelines to obtain sensing performance guarantees by deriving analytical bounds on saturation and analog-to-digital quantization in relation to the multipath SI level. By selecting standard reference codebooks in our simulations, we show how our method substantially improves the signal-to-noise ratio for sensing with little impact on 5G-NR communication.

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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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arXiv2025

Two-timescale joint power control and beamforming design with applications to cell-free massive MIMO

In this study we derive novel optimal algorithms for joint power control and beamforming design in modern large-scale MIMO systems, such as those based on the cell-free massive MIMO and XL-MIMO concepts. In particular, motivated by the need for scalable system architectures, we formulate and solve nontrivial two-timescale extensions of the classical uplink power minimization and max-min fair resource allocation problems. In our formulations, we let the beamformers be functions mapping partial instantaneous channel state information (CSI) to beamforming weights, and we jointly optimize these functions and the power control coefficients based on long-term statistical CSI. This long-term approach mitigates the severe scalability issues of competing short-term iterative algorithms in the literature, where a central controller endowed with global instantaneous CSI must solve a complex optimization problem for every channel realization, hence imposing very demanding requirements in terms of computational complexity and signaling overhead. Moreover, our approach outperforms the available long-term approaches, which do not jointly optimize powers and beamformers. The obtained optimal long-term algorithms are then illustrated and compared against existing short-term and long-term algorithms via numerical simulations in a cell-free massive MIMO setup with different levels of cooperation.

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arXiv2025

On the Optimal Performance of Distributed Cell-Free Massive MIMO with LoS Propagation

In this study, we revisit the performance analysis of distributed beamforming architectures in dense user-centric cell-free massive multiple-input multiple-output (mMIMO) systems in line-of-sight (LoS) scenarios. By incorporating a recently developed optimal distributed beamforming technique, called the team minimum mean square error (TMMSE) technique, we depart from previous studies that rely on suboptimal distributed beamforming approaches for LoS scenarios. Supported by extensive numerical simulations that follow 3GPP guidelines, we show that such suboptimal approaches may often lead to significant underestimation of the capabilities of distributed architectures, particularly in the presence of strong LoS paths. Considering the anticipated ultra-dense nature of cell-free mMIMO networks and the consequential high likelihood of strong LoS paths, our findings reveal that the team MMSE technique may significantly contribute in narrowing the performance gap between centralized and distributed architectures.

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arXiv2025

A Comparison among Single Carrier, OFDM, and OTFS in mmWave Multi-Connectivity Downlink Transmissions

In this paper, we perform a comparative study of common wireless communication waveforms, namely the single carrier (SC), orthogonal frequency-division multiplexing (OFDM), and orthogonal time-frequency-space (OTFS) modulation in a millimeter wave (mmWave) downlink multi-connectivity scenario, where multiple access points (APs) jointly serve a given user under imperfect time and frequency synchronization errors. For a fair comparison, all the three waveforms are evaluated using variants of common frequency domain equalization (FDE). To this end, a novel cross domain iterative detection for OTFS is proposed. The performance of the different waveforms is evaluated numerically in terms of pragmatic capacity. The numerical results show that OTFS significantly outperforms SC and OFDM at cost of reasonably increased complexity, because of the low cyclic-prefix (CP) overhead and the effectiveness of the proposed detection.

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arXiv2025

Semantic Security with Infinite Dimensional Quantum Eavesdropping Channel

We propose a new proof method for direct coding theorems for wiretap channels where the eavesdropper has access to a quantum version of the transmitted signal on an infinite-dimensional Hilbert space and the legitimate parties communicate through a classical channel or a classical input, quantum output (cq) channel. The transmitter input can be subject to an additive cost constraint, which specializes to the case of an average energy constraint. This method yields errors that decay exponentially with increasing block lengths. Moreover, it provides a guarantee of a quantum version of semantic security, which is an established concept in classical cryptography and physical layer security. Therefore, it complements existing works which either do not prove the exponential error decay or use weaker notions of security. The main part of this proof method is a direct coding result on channel resolvability which states that there is only a doubly exponentially small probability that a standard random codebook does not solve the channel resolvability problem for the cq channel. Semantic security has strong operational implications meaning essentially that the eavesdropper cannot use its quantum observation to gather any meaningful information about the transmitted signal. We also discuss the connections between semantic security and various other established notions of secrecy.

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arXiv2025

RIS-assisted Physical Layer Security

We propose a reconfigurable intelligent surface (RIS)-assisted wiretap channel, where the RIS is strategically deployed to provide a spatial separation to the transmitter, and orthogonal combiners are employed at the legitimate receiver to extract the data streams from the direct and RIS-assisted links. Then we derive the achievable secrecy rate under semantic security for the RIS-assisted channel and design an algorithm for the secrecy rate optimization problem. The simulation results show the effects of total transmit power, the location and number of eavesdroppers on the security performance.

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arXiv2024

Reconfigurable Intelligent Surfaces for 6G Mobile Networks: An Industry R&D Perspective

The reconfigurable intelligent surface (RIS) technology is a potential solution to enhance network capacity and coverage without significant investment in additional infrastructure in 6G networks. This work highlights the interest of the mobile communication industry in RIS, and discusses the development of liquid crystal-based RIS for improved energy efficiency and coverage in the millimeter-wave band. Furthermore, the paper discusses perspectives and insights from an industry R&D point of view, addressing relevant use cases, technical requirements, implementation challenges, and practical considerations for RIS deployment optimization in the context of 6G networks. A hardware design of an RIS with liquid crystal at 28 GHz is presented. A propagation model for RIS as a new part of the system architecture is discussed, with approaches of semi-empirical models, geometric models, and their combination through the application of artificial intelligence/machine learning. Finally, a channel model for deployment optimization and dimensioning is presented, with the findings that a rather large RIS is favorable for coverage improvement, as well as greater attenuation at higher frequencies combined with a smaller RIS size.

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arXiv2024

Robust mmWave/sub-THz multi-connectivity using minimal coordination and coarse synchronization

This study investigates simpler alternatives to coherent joint transmission for supporting robust connectivity against signal blockage in mmWave/sub-THz access networks. By taking an information-theoretic viewpoint, we demonstrate analytically that with a careful design, full macrodiversity gains and significant SNR gains can be achieved through canonical receivers and minimal coordination and synchronization requirements at the infrastructure side. Our proposed scheme extends non-coherent joint transmission by employing a special form of diversity to counteract artificially induced deep fades that would otherwise make this technique often compare unfavorably against standard transmitter selection schemes. Additionally, the inclusion of an Alamouti-like space-time coding layer is shown to recover a significant fraction of the optimal performance. Our conclusions are based on a statistical single-user multi-point intermittent block fading channel model that, although simplified, enables rigorous ergodic and outage rate analysis, while also considering timing offsets due to imperfect delay compensation. In addition, we validate our theoretical approach by means of deterministic ray-tracing simulations that capture the essential features of next generation mmWave/sub-THz communications.

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arXiv2024

Two-timescale weighted sum-rate maximization for large cellular and cell-free massive MIMO

We reconsider the problem of joint power control and beamforming design to maximize the weighted sum rate in large and potentially cell-free massive MIMO networks. In contrast to the available short-term methods, where an iterative algorithm is run for every instantaneous channel realization, we derive an iterative algorithm that can be run only sporadically leveraging known channel statistics, with minor performance loss. In addition, our algorithm also applies to the design of non-trivial cooperative beamforming schemes subject to limited sharing of instantaneous channel state information. Furthermore, our algorithm generalizes and outperforms the competing long-term methods from the massive MIMO literature, which are restricted to long-term power control only or to long-term joint power control and large-scale fading decoding design.

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arXiv2024

UL-DL duality for cell-free massive MIMO with per-AP power and information constraints

We derive a novel uplink-downlink duality principle for optimal joint precoding design under per-transmitter power and information constraints in fading channels. The information constraints model limited sharing of channel state information and data bearing signals across the transmitters. The main application is to cell-free networks, where each access point (AP) must typically satisfy an individual power constraint and form its transmit signal using limited cooperation capabilities. Our duality principle applies to ergodic achievable rates given by the popular hardening bound, and it can be interpreted as a nontrivial generalization of a previous result by Yu and Lan for deterministic channels. This generalization allows us to study involved information constraints going beyond the simple case of cluster-wise centralized precoding covered by previous techniques. Specifically, we show that the optimal joint precoders are, in general, given by an extension of the recently developed team minimum mean-square error method. As a particular yet practical example, we then solve the problem of optimal local precoding design in user-centric cell-free massive MIMO networks subject to per-AP power constraints.

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arXiv2024

Unlocking the Potential of Local CSI in Cell-Free Networks with Channel Aging and Fronthaul Delays

It is generally believed that downlink cell-free networks perform best under centralized implementations where the local channel state information (CSI) acquired by the access-points (AP) is forwarded to one or more central processing units (CPU) for the computation of the joint precoders based on global CSI. However, mostly due to limited fronthaul capabilities, this procedure incurs some delay that may lead to partially outdated precoding decisions and hence performance degradation. In some scenarios, this may even lead to worse performance than distributed implementations where the precoders are locally computed by the APs based on partial yet timely local CSI. To address this issue, this study considers the problem of robust precoding design merging the benefits of timely local CSI and delayed global CSI. As main result, we provide a novel distributed precoding design based on the recently proposed team minimum mean-square error method. As a byproduct, we also obtain novel insights related to the AP-CPU functional split problem. Our main conclusion, corroborated by simulations, is that the opportunity of performing some local precoding computations at the APs should not be neglected, even in centralized implementations.

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arXiv2024

Optimized Detection with Analog Beamforming for Monostatic Integrated Sensing and Communication

In this paper, we formalize an optimization framework for analog beamforming in the context of monostatic integrated sensing and communication (ISAC), where we also address the problem of self-interference in the analog domain. As a result, we derive semidefinite programs to approach detection-optimal transmit and receive beamformers, and we devise a superiorized iterative projection algorithm to approximate them. Our simulations show that this approach outperforms the detection performance of well-known design techniques for ISAC beamforming, while it achieves satisfactory self-interference suppression.

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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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arXiv2024

Towards Secure Over-The-Air Computation

We propose a new method to protect Over-The-Air (OTA) computation schemes against passive eavesdropping. Our method uses a friendly jammer whose signal is -- contrary to common intuition -- stronger at the legitimate receiver than it is at the eavesdropper. We focus on the computation of arithmetic averages over an OTA channel. The derived secrecy guarantee translates to a lower bound on the eavesdropper's mean square error while the question of how to provide operationally more significant guarantees such as semantic security remains open for future work. The key ingredients in proving the security guarantees are a known result on channel resolvability and a generalization of existing achievability results on coding for compound channels.

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arXiv2024

Towards Bridging the Gap between Near and Far-Field Characterizations of the Wireless Channel

The "near-field" propagation modeling of wireless channels is necessary to support sixth-generation (6G) technologies, such as intelligent reflecting surface (IRS), that are enabled by large aperture antennas and higher frequency carriers. As the conventional far-field model proves inadequate in this context, there is a pressing need to explore and bridge the gap between near and far-field propagation models. Although far-field models are simple and provide computationally efficient solutions for many practical applications, near-field models provide the most accurate representation of wireless channels. This paper builds upon the foundations of electromagnetic wave propagation theory to derive near and far-field models as approximations of the Green's function (Maxwell's equations). We characterize the near and far-field models both theoretically and with the help of simulations in a line-of-sight (LOS)-only scenario. In particular, for two key applications in multiantenna systems, namely, beamforming and multiple-access, we showcase the advantages of using the near-field model over the far-field, and present a novel scheduling scheme for multiple-access in the near-field regime. Our findings offer insights into the challenge of incorporating near-field models in practical wireless systems, fostering enhanced performance in future communication technologies.

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arXiv2024

Distributed fixed-point algorithms for dynamic convex optimization over decentralized and unbalanced wireless networks

We consider problems where agents in a network seek a common quantity, measured independently and periodically by each agent through a local time-varying process. Numerous solvers addressing such problems have been developed in the past, featuring various adaptations of the local processing and the consensus step. However, existing solvers still lack support for advanced techniques, such as superiorization and over-the-air function computation (OTA-C). To address this limitation, we introduce a comprehensive framework for the analysis of distributed algorithms by characterizing them using the quasi-Fejér type algorithms and an extensive communication model. Under weak assumptions, we prove almost sure convergence of the algorithm to a common estimate for all agents. Moreover, we develop a specific class of algorithms within this framework to tackle distributed optimization problems with time-varying objectives, and, assuming that a time-invariant solution exists, prove its convergence to a solution. We also present a novel OTA-C protocol for consensus step in large decentralized networks, reducing communication overhead and enhancing network autonomy as compared to the existing protocols. The effectiveness of the algorithm, featuring superiorization and OTA-C, is demonstrated in a real-world application of distributed supervised learning over time-varying wireless networks, highlighting its low-latency and energy-efficiency compared to standard approaches.

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