arXiv subjects

Norman Franchi

Publications and source records attributed to Norman Franchi.

16 recordsLinked to original sources

arXiv2026

Optimal Measurement Point Selection for Radio Environment Maps: A Two-Stage Approach

Electromagnetic (EM) situational awareness is essential for the deployment and operation of local and temporary non-public networks (NPNs). Radio Environment Maps (REMs) provide an effective basis for network planning and operation by capturing the prevailing EM conditions. However, efficient measurement location selection for REM construction remains a key challenge, particularly under strict time and cost constraints. This paper presents a two-stage measurement selection framework that employs Optimal Experimental Design (OED)-based strategies to generate a reduced candidate set during a pre-selection phase. In a subsequent optimization stage, combinatorial optimization is applied to select an improved subset of measurement locations by minimizing the Mean Absolute Error (MAE) of the resulting REM, using a scenario-specific propagation simulation model. The results demonstrate that the proposed framework reduces the number of required measurement locations while consistently improving REM construction accuracy across all evaluated strategies. Validation using real-world measurement data confirms the practical applicability of the approach, with the optimization stage achieving MAE reductions from about 4% to around 50%, independent of the chosen pre-selection strategy.

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arXiv2026

TDMA Based Communications Control Co-Design for Cooperative Carrying: Delay Calibration and Sampling-Rate Optimization

Multi robot teams performing cooperative transportation face a fundamental challenge: maintaining stable control while keeping communications efficient. This paper investigates how adaptive sampling time adjustment informed by measured network delay and strategic leader rotation can distribute wireless load fairly across the team. We use physics based simulation in MuJoCo with realistic wireless modeling, including time division multiple access, medium access control, jitter, queueing, and packet loss, to evaluate three control approaches: fixed sampling with static leadership, dynamic sampling with static leadership, and dynamic sampling with rotating leadership. Our results reveal an important trade off: dynamic sampling effectively reduces communications overhead without compromising control performance, while rotating the leader role meaningfully improves how fairly airtime is distributed all with negligible impact on the team carrying ability. to the best of our knowledge, being among the first to jointly examine dynamic sampling, rotating leadership, and wireless protocol interactions in physicsrealistic multi robot cooperation, this work provides practical guidance for deploying coordinated robotic teams in real world settings where communications resources are limited.

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arXiv2026

Multi-Agent Reinforcement Learning for Base Station Placement in TDOA-Based Localization

Accurate localization of devices is a key capability for emerging 5G and 6G networks and depends on effective base station (BS) placement. Conventional geometry-based approaches such as Geometric Dilution of Precision (GDOP) ignore realistic propagation effects such as Non-Line of Sight (NLOS) shadowing and multipath-induced Time of Arrival (TOA) bias caused by buildings. This paper proposes a ray-tracing-assisted Multi-Agent Reinforcement Learning (MARL) framework for environment-aware BS placement in Time Difference of Arrival (TDOA) localization systems. Proximal Policy Optimization (PPO) agents are trained on Channel Impulse Responses (CIRs) generated from a detailed 3D model of a university campus. Each agent cooperatively places one BS while optimizing a shared reward that combines localization accuracy and coverage. The approach is evaluated on five campus segments with varying propagation characteristics. Results show that the learned policy achieves localization accuracy comparable to conventional GDOP-based placement, lowering the average localization Mean Absolute Error (MAE) by about 3 % relative to the stronger (mean-optimized) geometric baseline. The behavior is segment-dependent, with a clear improvement on individual segments (up to about 14 %) and comparable or slightly higher error on the others. These findings indicate that incorporating site-specific propagation data into the placement process can match and selectively improve upon purely geometric strategies, motivating further work toward consistent gains.

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arXiv2026

CRLB-Driven Beamforming and Power Allocation for Multi-BS Cooperative ISAC Networks

This paper presents a Cram\'er-Rao lower bound (CRLB)-driven beamforming (BF) and power allocation (PA) framework for cooperative integrated sensing and communication (ISAC) networks, where a set of multi-antenna base stations (BSs) jointly serve multiple users and simultaneously perform multi-static target estimation. In our design, we investigate how the position error bound (PEB) and velocity error bound (VEB) can be exploited and incorporated into BF and PA optimization. The design leveraging PEB and VEB as metrics directly characterizes sensing accuracy. First, we propose a semidefinite programming (SDP)-based BF, where the PEB and VEB constraints are handled by double Schur complements, and the rank-1 constraints of the BF covariance matrices are relaxed by semidefinite relaxation (SDR), whose tightness is proved using the Karush-Kuhn-Tucker (KKT) conditions. Addressing the complexity, we further develop a two-stage PA algorithm, where the communication and sensing beams are formed by the regularized zero-forcing (RZF) and null-space projection (NSP) methods, respectively, and the communication and sensing PAs are solved sequentially by second-order cone programming (SOCP). Although the PA algorithm sacrifices the degrees of freedom, its performance shows a slight gap compared to BF in the simulation, while the execution time of around 25 ms demonstrates its applicability in dynamic environments.

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arXiv2026

Fundamental Sensing Limits of 6G Cooperative MIMO-ISAC Networks: Joint Position-Velocity CRLB and Decoupling Analysis

This paper presents a Cram\'er-Rao lower bound (CRLB)-based performance bound analysis of cooperative multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) networks. We first show the CRLB transformation of the signal-level parameters to the state parameters (position and velocity) in cooperative ISAC networks. Unlike existing studies that primarily ignored coupling between position and velocity in the Fisher information matrix (FIM), we derive the full FIM and the corresponding exact CRLB. Particularly, the results of multi-monostatic sensing, multi-bistatic sensing, and their hybrid are discussed. Addressing the complexity and tractability, we simplify the FIM and CRLB by excluding the coupling terms between the position and velocity, and provide a criterion for determining whether the simplification is valid. The simplified CRLB benefits from low computational complexity and provides a tractable and reliable performance metric for optimization problems such as resource allocation and beamforming. Finally, the position and velocity CRLBs and the simplification-induced error are examined in the simulation. The results demonstrate that the simplified CRLB can be applied in general cases. Based on the simulation results, the impact of resource and geometric parameters on position and velocity error bounds, and the validity of the simplified CRLBs is explained through the corresponding CRLB expressions.

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arXiv2026

Multiuser MIMO-AFDM Beamforming for ISAC in Doubly Dispersive Channels

Integrated sensing and communication (ISAC) in high-mobility channels requires waveform and beamforming designs that are robust to delay-Doppler dispersion. With this in mind, in this paper we study a monostatic multiuser multiple-input multiple-output (MIMO) affine frequency division multiplexing (AFDM) downlink system. We develop a discrete affine Fourier transform (DAFT)-domain model that preserves Doppler-induced inter-bin coupling and derive a data-aided delay-Doppler detector. The expected matched-bin detector signal-to-noise ratio (SNR) is shown to be proportional to a transmit-covariance beampattern, which leads to a detector-SNR-based sector-illumination constraint. The resulting sensing-constrained weighted sum-rate maximization problem is solved using a combined weighted minimum mean squared error (WMMSE) and majorization-minimization (MM) formulation. Simulations show that the proposed AFDM design outperforms its orthogonal frequency division multiplexing (OFDM) counterpart in terms of the rate-sensing tradeoff, robustness to Doppler, and delay-Doppler sensing quality.

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arXiv2026

On the Feasibility of Passive Bistatic ISAC Based on Unmodified LoRa

Integrated Sensing and Communication (ISAC) enables sensing capabilities by reusing communication signals, making it particularly attractive for large-scale deployments through signals of opportunity. While most existing ISAC research targets wideband systems, Low Power Wide Area Network (LPWAN) technologies such as LoRa remain largely unexplored from a radar-like sensing perspective. Existing LoRa-based approaches mainly focus on motion detection or require modifications of the communication waveform, limiting their applicability in deployed networks. This paper investigates the feasibility of radar-like sensing using unmodified LoRa communication signals as signals of opportunity in a purely passive bistatic ISAC configuration. The proposed approach focuses on Doppler-based sensing to enable target separation and super-resolved target estimation without interfering with existing LoRa network operation. The analytically derived sensing capabilities are compared against simulation results and validated through bistatic measurements using two USRP B210 software-defined radios, confirming the feasibility of Doppler-based LoRa sensing under practical conditions and revealing relevant implementation challenges. The results demonstrate that LoRa-based ISAC enables highly scalable, large-area, low-resolution sensing by leveraging existing infrastructure, providing a complementary sensing capability to area-limited high-resolution 6G ISAC systems, and a foundation for future multi-node and data fusion extensions.

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arXiv2026

On Unified CRLB Framework from Generic Signals to ISAC Waveforms with Virtual Array Sensing

This paper presents a unified Cram\'er-Rao lower bound (CRLB) framework for signal-level parameters in integrated sensing and communications (ISAC)-enabled radar systems. Starting from the generic signal model, we analyze the coupling between delay and Doppler in the Fisher information matrix (FIM), which is unsolved and often overlooked in relevant studies. Addressing this issue, we derive the conditions under which the coupling terms can be eliminated and demonstrate that these conditions are typically satisfied for ISAC-enabled waveforms. Afterward, the CRLBs of representative ISAC waveforms are derived within the unified framework, enabling consistent and comparable analysis across the waveforms and avoiding model-dependent discrepancies. Further, the framework is extended to virtual array (VA) sensing systems, where the impact of different multiplexing schemes is analyzed. Simulation results demonstrate the consistency between the CRLBs derived from the proposed framework and those obtained from waveform-specific analyses. The proposed framework shows strong generality, waveform-compatibility, and flexibility, offering a versatile tool for the CRLB analysis of various waveforms, including those lacking existing analytical results.

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arXiv2026

Convergence Time Distributions for Max-Consensus over Unreliable Networks

This paper proposes the LiFE-CD algorithm for convergence time analysis of the max-consensus algorithm in multi-agent systems under Bernoulli-distributed link failures. Unlike existing approaches, which either assume ideal communication or provide asymptotic upper bounds on the expected convergence time, LiFE-CD deterministically computes the full probability distribution of the convergence time from network topology and individual link failure probabilities, without simulation. The full probability distribution enables deadline-aware protocol design with specified reliability guarantees. Based on geometrically distributed link delays, the proposed algorithm iteratively reduces the given network topology considering both unicast and broadcast transmissions. LiFE-CD yields exact results for acyclic networks and, for cyclic networks, tight upper bounds on the convergence time via shortest-path spanning tree construction. Numerical results confirm analytical exactness for acyclic networks, validate tightness for cyclic networks, and demonstrate improvement over existing approaches. Our complexity analysis shows reduced computational cost compared to Monte Carlo simulations, while eliminating stochastic variability and enhancing reproducibility. All results extend directly to min-consensus by structural equivalence.

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arXiv2025

Analysis of an Architecture for Integrated Sensing and Communication in 5G OpenRAN

This paper analyzes the functional requirements and architectural considerations for Integrated Sensing and Communication ( ISAC) in a 5G Open Radio Access Network (OpenRAN) environment, with emphasis on secure and modular deployment. Focusing on a mono-static, half-duplex sensing approach, it evaluates radar setup options, signal types, and processing placement within the Radio Access Network ( RAN), considering performance and security implications. The proposed architecture minimizes hardware modifications by leveraging sniffer Radio Units (RU s) and existing OpenRAN fronthaul interfaces, while protecting sensitive In-phase and Quadrature (I/Q) data and control traffic against potential attacks. Security threats, such as passive sensing, spoofing, and privacy violations, are mapped to mitigation strategies within the OpenRAN framework. The result is a deployment blueprint applicable to both Public Land Mobile Networks ( PLMNs) and Non-Public Networks (NPNs), supporting future 6G ISAC capabilities in a standards-compliant manner.

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arXiv2025

Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective

The sixth-generation wireless communications (6G) is often labeled as "connected intelligence". Radio sensing, aligned with machine learning (ML) and artificial intelligence (AI), promises, among other benefits, breakthroughs in the system's ability to perceive the environment and effectively utilize this awareness. This article offers a tutorial-style survey of AI and ML approaches to enhance the sensing capabilities of next-generation wireless networks. To this end, while staying in the framework of integrated sensing and communication (ISAC), we expand the term "sensing" from radar, via spectrum sensing, to miscellaneous applications of radio sensing like non-cooperative transmitter localization. We formulate the problems, explain the state-of-the-art approaches, and detail AI-based techniques to tackle various objectives in the context of wireless sensing. We discuss the advantages, enablers, and challenges of integrating various sensing capabilities into an envisioned AI-powered multimodal multi-task network. In addition to the tutorial-style core of this work based on direct authors' involvement in 6G research problems, we review the related literature, and provide both a good start for those entering this field of research, and a topical overview for a general reader with a background in wireless communications

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arXiv2025

Hardware Limitations and Optimization Approach in 1-Bit RIS Design at 28 GHz

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for electromagnetic (EM) wave manipulation, offering unprecedented control over wave reflections compared to traditional metallic reflectors. By utilizing an array of tunable elements, RIS can steer and shape electromagnetic waves to enhance signal quality in wireless communication and radar systems. However, practical implementations face significant challenges due to hardware limitations and phase quantization errors. In this work, a 1-bit RIS prototype operating at 28 GHz is developed to experimentally evaluate the impact of hardware constraints on RIS performance. Unlike conventional studies that model RIS as an ideal phase-shift matrix, this study accounts for physical parameters that influence the actual reflection pattern. In particular, the presence of specular reflection due to hardware limitations is investigated. Additionally, the effects of phase quantization errors, which stem from the discrete nature of RIS elements, are analyzed, and a genetic algorithm (GA)-based optimization is introduced to mitigate these errors. The proposed optimization strategy effectively reduces gain degradation at the desired angle caused by 1-bit quantization, enhancing the overall performance of RIS. The effectiveness of the approach is validated through measurements, underscoring the importance of advanced phase control techniques in improving the functionality of RIS.

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arXiv2024

How connected cars could capture cloud dynamics -- first evidence from two simulation scenarios

The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image-based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal-based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40 %, the root mean square errors for both sensor networks are still below 5 m/s. In conclusion, the spatio-temporal characteristics of a vehicle network offer some potential for estimating cloud movements.

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arXiv2024

Resilience-by-Design in 6G Networks: Literature Review and Novel Enabling Concepts

The sixth generation (6G) mobile communication networks are expected to intelligently integrate into various aspects of modern digital society, including smart cities, homes, health-care, transportation, and factories. While offering a multitude of services, it is likely that societies become increasingly reliant on 6G infrastructure. Any disruption to these digital services, whether due to human or technical failures, natural disasters, or terrorism, would significantly impact citizens' daily lives. Hence, 6G networks need not only to provide high-performance services but also to be resilient in maintaining essential services in the face of potentially unknown challenges. This paper provides a general review of the state of the art on resilient systems, definitions, concepts, and approaches. Moreover, it introduces a comprehensive concept, i.e., resilience-by-design (RBD), in three different levels for designing resilient 6G communication networks, summarizing our initial studies within the German Open6GHub project. First, we outline the general RBD enabling principles and discuss their related sub-categories. Next, adopting an interdisciplinary approach, we propose to embed these principles across all 6G layers/perspectives including electronics, physical channel, network components and functions, networks, services, and cross-layer and cross-infrastructure considerations and discuss their challenges. We further elaborate the RBD principles and their realizations along with several 6G use-cases. The paper is concluded by presenting a comprehensive list of open problems for future research on 6G resilience.

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arXiv2023

Enabling Mobility-Oriented JCAS in 6G Networks: An Architecture Proposal

Sensing plays a crucial role in autonomous and assisted vehicular driving, as well as in the operation of autonomous drones. The traditional segregation of communication and onboard sensing systems in mobility applications is due to be merged using Joint Communication and Sensing (JCAS) in the development of the 6G mobile radio standard. The integration of JCAS functions into the future road traffic landscape introduces novel challenges for the design of the 6G system architecture. Special emphasis will be placed on facilitating direct communication between road users and aerial drones. In various mobility scenarios, diverse levels of integration will be explored, ranging from leveraging communication capabilities to coordinate different radars to achieving deep integration through a unified waveform. In this paper, we have identified use cases and derive five higher-level Tech Cases (TCs). Technical and functional requirements for the 6G system architecture for a device-oriented JCAS approach will be extracted from the TCs and used to conceptualize the architectural views.

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arXiv2018

Reliable Low Latency Wireless Communication Enabling Industrial Mobile Control and Safety Applications

Advanced industrial applications for human-machine interaction such as augmented reality support for maintenance works or mobile control panels for operating production facility set high demands on underlying wireless connectivity solution. Based on 802.11 standard, this paper proposes a concept of a new system, which is capable of those requirements. For increasing reliability, an agile triple-band (2.4 GHz, 5 GHz and 60 GHz) communication system can be used. In order to deal with latency and deterministic channel access, PHY and MAC techniques such as new waveforms or hybrid MAC schemes are investigated. Integration of precise localization introduces new possibilities for safety-critical applications.

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