PHY1 – ISAC
Wednesday, 3 June 2026, 8:30-10:00, room Sala de Conferencias 2 (2.1) (1st floor)
Session Chair: M. Carmen Aguayo (Univ. de Málaga, ES)
A Novel Multi-Static Target Localization Method Based on Distributed ISAC
Cevdet Tosun (TUBITAK BILGEM, Turkey & Istanbul Technical University, Turkey); Bora Bozkurt (TUBITAK BILGEM, Turkey & Istanbul Technical University (ITU), Turkey); Muhammed Mustafa Kadayıfçı and Mustafa Bilge (TUBITAK BILGEM, Turkey); Mehmet Akıncı (Istanbul Technical University, Turkey); Ali Gorcin (Istanbul Techical University, Turkey); Ibrahim Hokelek (Tubitak Bilgem, Turkey); Mehmet Kemal Ozdemir (Istanbul Medipol University, Turkey)
Integrated sensing and communication (ISAC) has attracted significant attention in recent years, driven by the growing demand for spectrum efficiency and the additional opportunities arising from leveraging communication signals for sensing. Among applications of ISAC, localization in cellular networks stands out as particularly important and is projected to become even more critical as cellular deployments become increasingly dense. This paper proposes a cellular localization method based on distributed ISAC that leverages physical random access channel (PRACH) preambles to estimate the positions of multiple targets, such as uncrewed aerial vehicles (UAVs), using direction of arrival (DoA) estimation. The simulation and laboratory measurement experiments demonstrate promising localization results of the distributed ISAC approach. Furthermore, the Cramér-Rao lower bound (CRLB) on localization error is derived as a theoretical benchmark for evaluating the estimation accuracy of the proposed method across different cell geometries.
Antenna Tilt Failure Detection and Estimation via Integrated Sensing and Communications
Samed Keşir and Batuhan Kaplan (Turkcell, Turkey); Emre Arslan (Koc University, Turkey); Ahmet Faruk Coskun (Turkcell, Turkey)
This paper addresses the critical sensitivity issue of narrow-beam communication systems to physical misalignments and exploits the potential of Integrated Sensing and Communications (ISAC) technology to propose a sensor-free antenna tilt failure detection and estimation framework. The proposed methods utilize environmental static clutter as geometric anchors to monitor systematic gain shifts in clutter heat maps. The proposed methods are introduced for precise antenna tilt detection and estimation using the standard 5G NR frame structure and two different waveforms. Numerical results show the potential of the proposed framework to enable autonomous, self healing network maintenance without the need for external sensors..
Bistatic ISAC: Practical Challenges and Solutions
Lucas Giroto and Marcus Henninger (Nokia Bell Labs, Germany); Alexander Felix (University of Stuttgart, Germany & Nokia Bell Labs, Germany); Maximilian Bauhofer (University of Stuttgart, Germany); Taewon Jeong and Umut Utku Erdem (Karlsruhe Institute of Technology, Germany); Stephan ten Brink (University of Stuttgart, Germany); Thomas Zwick (Karlsruhe Institute of Technology (KIT), Germany); Benjamin Nuss (Technical University of Munich, Germany); Silvio Mandelli (Nokia Bell Labs, Germany)
This article presents and discusses challenges and solutions for practical issues in bistatic integrated sensing and communication (ISAC) in 6G networks. Considering orthogonal frequency-division multiplexing as the adopted waveform, a discussion on system design aiming to achieve both a desired sensing key performance indicators and limit the impact of hardware impairments is presented. In addition, signal processing techniques to enable over-the-air synchronization and generation of periodograms with range, Doppler shift, and angular information are discussed. Simulation results are then presented for a cellular-based ISAC scenario considering system parameterization compliant to current 5G and, finally, a discussion on open challenges for future deployments is presented.
Communication-Centric Partially Connected Hybrid Beamforming Design for ISAC Systems
Leonardo Leyva (Instituto de Telecomunicações); Daniel Castanheira (Instituto de Telecomunicações (IT)/University of Aveiro, Portugal); Adão Silva (Instituto de Telelcomunicações, Portugal); Atílio Gameiro (Instituto de Telecomunicações / Universidade de Aveiro, Portugal)
This paper proposes a low-complexity hybrid analog–digital (HAD) beamforming design for integrated sensing and communication (ISAC) systems based on a partially connected architecture. Building upon a communication-centric ISAC framework, the objective is to maximize the downlink communication sum-rate while explicitly satisfying sensing and transmit power constraints in a multi-user and multi-beam scenario. By exploiting the structural properties of partial connectivity, we develop a new and low-complexity algorithm for the design of the analog and digital precoders, whose computational complexity scales linearly with the number of transmit antennas. Numerical results demonstrate that, while a performance gap with respect to fully connected architectures is observed due to reduced beamforming flexibility, the proposed approach achieves a favorable trade-off between communication performance and complexity, offering substantial gains in computational efficiency and energy consumption, which are particularly attractive for large-scale and massive MIMO ISAC deployments.
Weather Estimation for Integrated Sensing and Communication
Victoria Palhares, Artjom Grudnitsky and Silvio Mandelli (Nokia Bell Labs, Germany)
One of the key features of sixth generation (6G) mobile communications will be integrated sensing and communication (ISAC). While the main goal of ISAC in standardization efforts is to detect objects, the byproducts of radar operations can be used to enable new services in 6G, such as weather sensing. Even though weather radars are the most prominent technology for weather detection and monitoring, they are expensive and usually neglect areas in close vicinity. To this end, we propose reusing the dense deployment of 6G base stations for weather sensing purposes by detecting and estimating weather conditions. We implement both a classifier and a regressor as a convolutional neural network trained across measurements with varying precipitation rates and wind speeds. We implement our approach in an ISAC proof-of-concept, and conduct a multi-week experiment campaign. Experimental results show that we are able to jointly and accurately classify weather conditions with accuracies of 99.38% and 98.99% for precipitation rate and wind speed, respectively. For estimation, we obtain errors of 1.2 mm/h and 1.5 km/h, for precipitation rate and wind speed, respectively. These findings indicate that weather sensing services can be reliably deployed in 6G ISAC networks, broadening their service portfolio and boosting their market value.
