arXiv subjects

Robert Schober

Publications and source records attributed to Robert Schober.

At least 73 records · Page 4Linked to original sources

arXiv2025

Far- versus Near-Field RIS Modeling and Beam Design

In this chapter, we investigate the mathematical foundation of the modeling and design of reconfigurable intelligent surfaces (RIS) in both the far- and near-field regimes. More specifically, we first present RIS-assisted wireless channel models for the far- and near-field regimes, discussing relevant phenomena, such as line-of-sight (LOS) and non-LOS links, rich and poor scattering, channel correlation, and array manifold. Subsequently, we introduce two general approaches for the RIS reflective beam design, namely optimization-based and analytical, which offer different degrees of design flexibility and computational complexity. Furthermore, we provide a comprehensive set of simulation results for the performance evaluation of the studied RIS beam designs and the investigation of the impact of the system parameters.

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arXiv2025

Sensing Accuracy Optimization for Multi-UAV SAR Interferometry with Data Offloading

The integration of unmanned aerial vehicles (UAVs) with radar imaging sensors has revolutionized the monitoring of dynamic and local Earth surface processes by enabling high-resolution and cost-effective remote sensing. This paper investigates the optimization of the sensing accuracy of a UAV swarm deployed to perform multi-baseline interferometric synthetic aperture radar (InSAR) sensing. In conventional single-baseline InSAR systems, only one synthetic aperture radar (SAR) antenna pair acquires two SAR images from two distinct angles to generate a digital elevation model (DEM) of the target area. However, multi-baseline InSAR extends this concept by aggregating multiple acquisitions from different angles, thus, significantly enhancing the vertical accuracy of the DEM. The heavy computations required for this process are performed on the ground and, therefore, the radar data is transmitted in real time to a ground station (GS) via a frequency-division multiple access (FDMA) air-to-ground backhaul link. This work focuses on improving the sensing precision by minimizing the height error of the averaged DEM while simultaneously ensuring sensing and communication quality-of-service (QoS). To this end, the UAV formation, velocity, and communication power allocation are jointly optimized using evolutionary algorithms (EAs). Our approach is benchmarked against established optimization methods, including genetic algorithms (GAs), simulated annealing (SA), and deep reinforcement learning (DRL) techniques. Numerical results show that the proposed solution outperforms these baseline schemes and achieves sub-decimeter vertical accuracy in several scenarios. These findings underline the potential of coordinated UAV swarms for delivering high-precision and real-time Earth observations through radar interferometry.

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arXiv2025

From Literature to Insights: Methodological Guidelines for Survey Writing in Communications Research

The rapid growth of communications and networking research has created an unprecedented demand for high-quality survey and tutorial papers that can synthesize vast bodies of literature into coherent understandings and actionable insights. However, writing impactful survey papers presents multifaceted challenges that demand substantial effort beyond traditional research article composition. This article provides a systematic, practical roadmap for prospective authors in the communications research community, drawing upon extensive editorial experience from premier venues such as the IEEE Communications Surveys & Tutorials. We present structured guidelines covering seven essential aspects: strategic topic selection with novelty and importance, systematic literature collection, effective structural organization, critical review writing, tutorial content development with emphasis on case studies, comprehensive illustration design that enhances comprehension, and identification of future directions. Our goal is to enable junior researchers to craft exceptional survey and tutorial articles that enhance understanding and accelerate innovation within the communications and networking research ecosystem.

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arXiv2025

Rotatable Antenna Enabled Wireless Communication and Sensing: Opportunities and Challenges

Non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest in recent years. Among them, rotatable antenna (RA) is an emerging technology that offers significant potential to enhance wireless communication and sensing performance by flexibly adjusting the boresight of directional antennas. Specifically, RA can flexibly reconfigure its boresight direction via mechanical or electronic means, thereby improving communication channel conditions and/or enhancing sensing resolution and range. In this article, we first provide an overview of RA, covering its hardware architectures and radiation pattern characterization. We then illustrate how RA improves communication performance through interference mitigation, spatial multiplexing, and flexible beamforming, as well as sensing capabilities in terms of coverage, resolution, and multi-target/dimensional sensing. Furthermore, we highlight representative applications of RA and discuss key design challenges in RA systems, including rotational scanning scheduling, channel estimation/sensing, boresight optimization, and RA configuration. Finally, both experimental and simulation results are provided to validate the performance gains achieved by RA for both communication and sensing. Leveraging its unique capabilities in flexible antenna/array rotation to adapt to various communication/sensing requirements and channel conditions, RA is poised to become a key enabler of future intelligent, resilient, and agile wireless networks.

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arXiv2025

Two-Timescale Sum-Rate Maximization for Movable Antenna Enhanced Systems

This paper studies a novel movable antenna (MA)-enhanced multiuser multiple-input multiple-output downlink system designed to improve wireless communication performance. We aim to maximize the average achievable sum rate through two-timescale optimization exploiting instantaneous channel state information at the receiver (I-CSIR) for receive antenna position vector (APV) design and statistical channel state information at the transmitter (S-CSIT) for transmit APV and covariance matrix design. We first decompose the resulting stochastic optimization problem into a series of short-term problems and one long-term problem. Then, a gradient ascent algorithm is proposed to obtain suboptimal receive APVs for the short-term problems for given I-CSIR samples. Based on the output of the gradient ascent algorithm, a series of convex objective/feasibility surrogates for the long-term problem are constructed and solved utilizing the constrained stochastic successive convex approximation (CSSCA) algorithm. Furthermore, we propose a planar movement mode for the receive MAs to facilitate efficient antenna movement and the development of a low-complexity primal-dual decomposition-based stochastic successive convex approximation (PDD-SSCA) algorithm, which finds Karush-Kuhn-Tucker (KKT) solutions almost surely. Our numerical results reveal that, for both the general and the planar movement modes, the proposed two-timescale MA-enhanced system design significantly improves the average achievable sum rate and the feasibility of the formulated problem compared to benchmark schemes.

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arXiv2025

Molecule Mixture Detection and Alphabet Design for Non-linear, Cross-reactive Receiver Arrays in MC

Air-based molecular communication (MC) has the potential to be one of the first MC systems to be deployed in real-world applications, enabled by existing sensor technologies such as metal-oxide semi-conductor (MOS) sensors. However, commercially available sensors usually exhibit non-linear and cross-reactive behavior, contrary to the idealizing assumptions about linear and perfectly molecule type-specific sensing often made in the MC literature. To address this gap, we propose a detector for molecule mixture communication with a general non-linear, cross-reactive receiver (RX) array that performs approximate maximum likelihood detection on the sensor outputs. Additionally, we introduce an algorithm for the design of mixture alphabets that accounts for the RX characteristics. We evaluate our detector and alphabet design algorithm through simulations that are based on measurements reported for two commercial MOS sensors. Our simulations demonstrate that the proposed detector achieves similar symbol error rates as data-driven methods without requiring large numbers of training samples and that the alphabet design algorithm outperforms methods that do not account for the RX characteristics. Since the proposed detector and alphabet design algorithm are also applicable to other chemical sensors, they pave the way for reliable air-based MC.

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arXiv2025

Wireless Energy Transfer Beamforming Optimization for Intelligent Transmitting Surface

Radio frequency (RF) wireless energy transfer (WET) is a promising technology for powering the growing ecosystem of Internet of Things (IoT) using power beacons (PBs). Recent research focuses on efficient PB architectures that can support numerous antennas. In this context, PBs equipped with intelligent surfaces present a promising approach, enabling physically large, reconfigurable arrays. Motivated by these advantages, this work aims to minimize the power consumption of a PB equipped with a passive intelligent transmitting surface (ITS) and a collocated digital beamforming-based feeder to charge multiple single-antenna devices. To model the PB's power consumption accurately, we consider power amplifiers nonlinearities, ITS control power, and feeder-to-ITS air interface losses. The resulting optimization problem is highly nonlinear and nonconvex due to the high-power amplifier (HPA), the received power constraints at the devices, and the unit-modulus constraint imposed by the phase shifter configuration of the ITS. To tackle this issue, we apply successive convex approximation (SCA) to iteratively solve convex subproblems that jointly optimize the digital precoder and phase configuration. Given SCA's sensitivity to initialization, we propose an algorithm that ensures initialization feasibility while balancing convergence speed and solution quality. We compare the proposed ITS-equipped PB's power consumption against benchmark architectures featuring digital and hybrid analog-digital beamforming. Results demonstrate that the proposed architecture efficiently scales with the number of RF chains and ITS elements. We also show that nonuniform ITS power distribution influences beamforming and can shift a device between near- and far-field regions, even with a constant aperture.

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arXiv2025

STAR-RIS-aided RSMA for the URLLC multi-user MIMO Downlink

Rate splitting multiple access (RSMA) is intrinsically amalgamated with simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RIS) to enhance energy efficiency (EE) of the finite block length (FBL) multiple-input multiple-output (MIMO) downlink. An alternating optimization-based algorithm is proposed to jointly optimize the transmit beamforming matrices, STAR-RIS configurations, and rate-splitting parameters. STAR-RIS attains 360-degree full-plane coverage, while RSMA provides a prominent gain by efficiently managing interference. Numerical results reveal a strong synergy between RSMA and STAR-RIS, demonstreating significant EE gains over reflective RIS and spatial division multiple access (SDMA).

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arXiv2025

Globally Optimal Movable Antenna-Enhanced multiuser Communication: Discrete Antenna Positioning, Motion Power Consumption, and Imperfect CSI

Movable antennas (MAs) represent a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by dynamically adapting the positions of antenna elements within a designated transmit area. In particular, by employing electro-mechanical MA drivers, the positions of the MA elements can be adjusted to shape a favorable spatial correlation for improving system performance. Although preliminary research has explored beamforming designs for MA systems, the intricacies of the power consumption and the precise positioning of MA elements are not well understood. Moreover, the assumption of perfect CSI adopted in the literature is impractical due to the significant pilot overhead and the extensive time to acquire perfect CSI. To address these challenges, we model the motion of MA elements through discrete steps and quantify the associated power consumption as a function of these movements. Furthermore, by leveraging the properties of the MA channel model, we introduce a novel CSI error model tailored for MA systems that facilitates robust resource allocation design. In particular, we optimize the beamforming and the MA positions at the BS to minimize the total BS power consumption, encompassing both radiated and MA motion power while guaranteeing a minimum required SINR for each user. To this end, novel algorithms exploiting the branch and bound (BnB) method are developed to obtain the optimal solution for perfect and imperfect CSI. Moreover, to support practical implementation, we propose low-complexity algorithms with guaranteed convergence by leveraging successive convex approximation (SCA). Our numerical results validate the optimality of the proposed BnB-based algorithms. Furthermore, we unveil that both proposed SCA-based algorithms approach the optimal performance within a few iterations, thus highlighting their practical advantages.

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arXiv2025

Rate Splitting Multiple Access for RIS-aided URLLC MIMO Broadcast Channels

The performance of modern wireless communication systems is typically limited by interference. The impact of interference can be even more severe in ultra-reliable and low-latency communication (URLLC) use cases. A powerful tool for managing interference is rate splitting multiple access (RSMA), which encompasses many multiple-access technologies like non-orthogonal multiple access (NOMA), spatial division multiple access (SDMA), and broadcasting. Another effective technology to enhance the performance of URLLC systems and mitigate interference is constituted by reconfigurable intelligent surfaces (RISs). This paper develops RSMA schemes for multi-user multiple-input multiple-output (MIMO) RIS-aided broadcast channels (BCs) based on finite block length (FBL) coding. We show that RSMA and RISs can substantially improve the spectral efficiency (SE) and energy efficiency (EE) of MIMO RIS-aided URLLC systems. Additionally, the gain of employing RSMA and RISs noticeably increases when the reliability and latency constraints are more stringent. Furthermore, RISs impact RSMA differently, depending on the user load. If the system is underloaded, RISs are able to manage the interference sufficiently well, making the gains of RSMA small. However, when the user load is high, RISs and RSMA become synergetic.

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arXiv2025

A Framework for Fractional Matrix Programming Problems with Applications in FBL MU-MIMO

An efficient framework is conceived for fractional matrix programming (FMP) optimization problems (OPs) namely for minimization and maximization. In each generic OP, either the objective or the constraints are functions of multiple arbitrary continuous-domain fractional functions (FFs). This ensures the framework's versatility, enabling it to solve a broader range of OPs than classical FMP solvers, like Dinkelbach-based algorithms. Specifically, the generalized Dinkelbach algorithm can only solve multiple-ratio FMP problems. By contrast, our framework solves OPs associated with a sum or product of multiple FFs as the objective or constraint functions. Additionally, our framework provides a single-loop solution, while most FMP solvers require twin-loop algorithms. Many popular performance metrics of wireless communications are FFs. For instance, latency has a fractional structure, and minimizing the sum delay leads to an FMP problem. Moreover, the mean square error (MSE) and energy efficiency (EE) metrics have fractional structures. Thus, optimizing EE-related metrics such as the sum or geometric mean of EEs and enhancing the metrics related to spectral-versus-energy-efficiency tradeoff yield FMP problems. Furthermore, both the signal-to-interference-plus-noise ratio and the channel dispersion are FFs. In this paper, we also develop resource allocation schemes for multi-user multiple-input multiple-output (MU-MIMO) systems, using finite block length (FBL) coding, demonstrating attractive practical applications of FMP by optimizing the aforementioned metrics.

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arXiv2025

Joint Radiation Power, Antenna Position, and Beamforming Optimization for Pinching-Antenna Systems with Motion Power Consumption

Pinching-antenna systems (PASS) have been recently proposed to improve the performance of wireless networks by reconfiguring both the large-scale and small-scale channel conditions. However, existing studies ignore the physical constraints of antenna placement and assume fixed antenna radiation power. To fill this research gap, this paper investigates the design of PASS taking into account the motion power consumption of pinching-antennas (PAs) and the impact of adjustable antenna radiation power. To that end, we minimize the average power consumption for a given quality-of-service (QoS) requirement, by jointly optimizing the antenna positions, antenna radiation power ratios, and transmit beamforming. To the best of the authors' knowledge, this is the first work to consider radiation power optimization in PASS, which provides an additional degree of freedom (DoF) for system design. The cases with both continuous and discrete antenna placement are considered, where the main challenge lies in the fact that the antenna positions affect both the magnitude and phase of the channel coefficients of PASS, making system optimization very challenging. To tackle the resulting unique obstacles, an alternating direction method of multipliers (ADMM)-based framework is proposed to solve the problem for continuous antenna movement, while its discrete counterpart is formulated as a mixed integer nonlinear programming (MINLP) problem and solved by the block coordinate descent (BCD) method. Simulation results validate the performance enhancement achieved by incorporating PA movement power assumption and adjustable radiation power into PASS design, while also demonstrating the efficiency of the proposed optimization framework. The benefits of PASS over conventional multiple-input multiple-output (MIMO) systems in mitigating the large-scale path loss and inter-user interference is also revealed.

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arXiv2025

Pinching-Antenna Systems with In-Waveguide Attenuation: Performance Analysis and Algorithm Design

Pinching-antenna systems have emerged as a promising flexible-antenna architecture for next-generation wireless networks, enabling enhanced adaptability and user-centric connectivity through antenna repositioning along waveguides. However, existing studies often overlook in-waveguide signal attenuation and in the literature, there is no comprehensive analysis on whether and under what conditions such an assumption is justified. This paper addresses this gap by explicitly incorporating in-waveguide attenuation into both the system model and algorithm design, and studying its impact on the downlink user data rates. We begin with a single-user scenario and derive a closed-form expression for the globally optimal antenna placement, which reveals how the attenuation coefficient and the user-to-waveguide distance jointly affect the optimal antenna position. Based on this analytical solution, we further provide a theoretical analysis identifying the system conditions under which the in-waveguide attenuation has an insignificant impact on the user achievable rate. The study is then extended to the multi-user multiple-input multiple-output setting, where two efficient algorithms are developed, based on the weighted minimum mean square error method and the maximum ratio combining method, to jointly optimize beamforming and antenna placement. Simulation results validate the efficacy of the proposed algorithms and demonstrate that pinching-antenna systems substantially outperform conventional fixed-antenna baselines, underscoring their potential for future flexible wireless communications.

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arXiv2025

MC for Agriculture: A Framework for Nature-inspired Sustainable Pest Control

In agriculture, molecular communication (MC) is envisioned as a framework to address critical challenges such as smart pest control. While conventional approaches mostly rely on synthetic plant protection products, posing high risks for the environment, harnessing plant signaling processes can lead to innovative approaches for nature-inspired sustainable pest control. In this paper, we investigate an approach for sustainable pest control and reveal how the MC paradigm can be employed for analysis and optimization. In particular, we consider a system where herbivore-induced plant volatiles (HIPVs), specifically methyl salicylate (MeSA), is encapsulated into microspheres deployed on deployed on plant leaves. The controlled release of MeSA from the microspheres, acting as transmitters (TXs), supports pest deterrence and antagonist attraction, providing an eco-friendly alternative to synthetic plant protection products. Based on experimental data, we investigate the MeSA release kinetics and obtain an analytical model. To describe the propagation of MeSA in farming environments, we employ a three dimensional (3D) advection-diffusion model, incorporating realistic wind fields which are predominantly affecting particle propagation, and solve it by a finite difference method (FDM). The proposed model is used to investigate the MeSA distribution for different TX arrangements, representing different practical microsphere deployment strategies. Moreover, we introduce the coverage effectiveness index (CEI) as a novel metric to quantify the environmental coverage of MeSA. This analysis offers valuable guidance for the practical development of microspheres and their deployment aimed at enhancing coverage and, consequently, the attraction of antagonistic insects.

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arXiv2025

A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication

The rapid evolution of forthcoming sixth-generation (6G) wireless networks necessitates the seamless integration of artificial intelligence (AI) with wireless communications to support emerging intelligent applications that demand both efficient communication and robust learning performance. This dual requirement calls for a unified framework of integrated learning and communication (ILAC), where AI enhances communication through intelligent signal processing and adaptive resource management, while wireless networks support AI model deployment by enabling efficient and reliable data exchanges. However, achieving this integration presents significant challenges in practice. Communication constraints, such as limited bandwidth and fluctuating channels, hinder learning accuracy and convergence. Simultaneously, AI-driven learning dynamics, including model updates and task-driven inference, introduce excessive burdens on communication systems, necessitating flexible, context-aware transmission strategies. Finally, we present a case study on a cost-to-performance optimization problem, where task assignments, model size selection, bandwidth allocation, and transmission power control are jointly optimized, considering computational cost, communication efficiency, and inference accuracy. Leveraging the Dinkelbach and alternating optimization algorithms, we offer a practical and effective solution to achieve an optimal balance between learning performance and communication constraints.

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arXiv2025

Closed-Loop Molecular Communication with Local and Global Degradation: Modeling and ISI Analysis

This paper presents a novel physics-based model for signal propagation in closed-loop molecular communication (MC) systems, which are particularly relevant for many envisioned biomedical applications, such as health monitoring or drug delivery within the closed-loop human cardiovascular system (CVS). Compared to open-loop systems, which are mostly considered in MC, closed-loop systems exhibit different characteristic effects influencing signaling molecule (SM) propagation. One key phenomenon are the periodic SM arrivals at the receiver (RX), leading to various types of inter-symbol interference (ISI) inherent to closed-loop system. To capture these characteristic effects, we propose an analytical model for the SM propagation inside closed-loop systems. The model accounts for arbitrary spatio-temporal SM release patterns at the transmitter (TX), and incorporates several environmental effects such as fluid flow, SM diffusion, and SM degradation. Moreover, to capture a wide range of practically relevant degradation and clearance mechanisms, the model includes both local removal (e.g., due to SM absorption into organs) and global removal (e.g., due to chemical degradation) of SMs. The accuracy of the proposed model is validated with three-dimensional (3-D) particle-based simulations (PBSs). Moreover, we utilize the proposed model to develop a rigorous characterization of the various types of ISI encountered in closed-loop MC systems.

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arXiv2025

Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning

Six-dimensional movable antenna (6DMA) has been identified as a new disruptive technology for future wireless systems to support a large number of users with only a few antennas. However, the intricate relationships between the signal carrier wavelength and the transceiver region size lead to inaccuracies in traditional far-field 6DMA channel model, causing discrepancies between the model predictions and the hybrid-field channel characteristics in practical 6DMA systems, where users might be in the far-field region relative to the antennas on the same 6DMA surface, while simultaneously being in the near-field region relative to different 6DMA surfaces. Moreover, due to the high-dimensional channel and the coupled position and rotation constraints, the estimation of the 6DMA channel and the joint design of the 6DMA positions and rotations and the transmit beamforming at the base station (BS) incur extremely high computational complexity. To address these issues, we propose an efficient hybrid-field generalized 6DMA channel model, which accounts for planar-wave propagation within individual 6DMA surfaces and spherical-wave propagation among different 6DMA surfaces. Furthermore, by leveraging directional sparsity, we propose a low-overhead channel estimation algorithm that efficiently constructs a complete channel map for all potential antenna position-rotation pairs while limiting the training overhead incurred by antenna movement. In addition, we propose a low-complexity design leveraging deep reinforcement learning (DRL), which facilitates the joint design of the 6DMA positions, rotations, and beamforming in a unified manner. Numerical results demonstrate that the proposed hybrid-field channel model and channel estimation algorithm outperform existing approaches and that the DRL-enhanced 6DMA system significantly surpasses flexible antenna systems.

cs.IT↗

arXiv2025

Energy Efficiency Optimization of Finite Block Length STAR-RIS-aided MU-MIMO Broadcast Channels

Energy-efficient designs are proposed for multi-user (MU) multiple-input multiple-output (MIMO) broadcast channels (BC), assisted by simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RIS) operating at finite block length (FBL). In particular, we maximize the sum energy efficiency (EE), showing that STAR-RIS can substantially enhance it. Our findings demonstrate that the gains of employing STAR-RIS increase when the codeword length and the maximum tolerable bit error rate decrease, meaning that a STAR-RIS is more energy efficient in a system with more stringent latency and reliability requirements.

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