ISLANDS: Integrated Sensing and communications for future vehicuLAr systems – a Network of Doctoral Students

Informazioni relative al progetto

ISLANDS

ID dell’accordo di sovvenzione: 101120544

DOI

10.3030/101120544

Data della firma CE 14 Luglio 2023

Data di avvio 1 Gennaio 2024

Data di completamento 30 Settembre 2028

Finanziato da

• Marie Skłodowska-Curie Actions (MSCA)

Costo totale

Nessun dato

Contributo UE

€ 2 598 782,40

Coordinato da

CONSORZIO NAZIONALE INTERUNIVERSITARIO PER LE TELECOMUNICAZIONI (CNIT)
Italy

Periodic Reporting for period 1 - ISLANDS (ISLANDS: Integrated Sensing and communications for future vehicuLAr systems – a Network of Doctoral Students)

Periodo di rendicontazione: 2024-01-01 al 2025-12-31

Sintesi del contesto e degli obiettivi generali del progetto

Project website: https://www.islands-mscadoctoralnetwork.eu/

ISLANDS targets Integrated Sensing and Communications (ISAC)—also known as joint communication and sensing / dual-function radar-communication—as a key 6G enabler. By pooling spectrum, power, and hardware (antennas/RF/DSP), ISAC can improve efficiency while enabling sensing-assisted communications (better channel estimation/beam tracking/handover) and communication-assisted sensing (enhanced localization, object detection, and environment mapping), unlocking new CAM services and business opportunities.

The automotive sector is a prime beneficiary: while 5G has introduced initial support for connected and automated mobility (e.g. low-latency features, slicing, localization, V2X/ToD), it is widely seen as insufficient for future reliability, safety, accuracy, and responsiveness requirements. ISAC’s dual use of signals and infrastructure is expected to be a decisive step toward 6G-grade CAM.

ISLANDS is structured as a Doctoral Network because delivering ISAC for vehicles is inherently interdisciplinary. The project will establish the theoretical and algorithmic foundations, develop early testbeds and simulators, and train a new generation of EU researchers specialized in vehicular ISAC.

The work is organized around four research objectives:

1) Theory: new mathematical tools and fundamental trade-offs for high-mobility DFRC/ISAC.

2) Algorithms: waveforms, beamforming, AI, and resource allocation for doubly selective channels.

3) Hardware: ISAC-tailored antennas and transceivers for vehicles and base stations.

4) Validation: over-the-air demos and a comprehensive open-access system-level simulator.

ISLANDS recruits 11 doctoral candidates, working across 11 integrated projects spanning multiple objectives.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

The project started on January 1, 2024. Doctoral candidates began their contracts between late 2024 and Q1 2025. Up to 31 December 2025, the following results were achieved:

DC-1 – Nima Imani (ACG-RC-1):
Developed an analytical coexistence framework for OFDM-ISAC and FMCW radars, deriving geometry-aware underlay power control rules. Linked radar SINR to sensing QoS and defined safe-distance/interference regions for mixed vehicular fleets.

DC-2 – Mubashar Sarfraz (ACG-RC-2):
Extended LTE-V2V and 5G-NR radar simulators for dense vehicular ISAC scenarios. Benchmarked Mode-3/Mode-4 resource allocation and derived MAC/PHY design guidelines for reliable cooperative awareness.

DC-3 – Isabela Buitron (BOSCH-1):
Contract not started during PR1; no activity to report.

DC-4 – Yuhao Zhang (CHAL-1):
Proposed UNILocPro, a hybrid model-based/unsupervised localization framework for mixed LoS/NLoS channels. Achieved near-supervised accuracy without labels and introduced a low-complexity deployable variant.

DC-5 – Maryam Darabi (CNIT-1):
Developed a Doppler-aware user-centric cell-free ISAC model with joint OFDM-based communication and sensing. Derived a GLRT detector and initiated SINR/SCNR validation under mobility.

DC-6 – Ahmed Elloumi (CNIT-2):
Extended BiLSTM-based AP–UE association with QoS prioritization for UC-CF networks. Developed near-field TRIS-aided XL-MIMO channel modeling for FR3 roadside ISAC.

DC-7 – Mehri Nikzad (CNIT-3):
Implemented a MATLAB simulator for radar–tag–reader backscatter ISAC. Translated analytical error expressions into computable functions for Monte-Carlo validation.

DC-8 – Paolo Tosi (NOKIA-1):
Benchmarked CFAR and CNN detection under hardware impairments and demonstrated mmWave NLOS sensing with a 5G PoC. Implemented CFAR + Kalman-based tracking for intrusion detection.

DC-9 – Xi Zhang (TAU-1):
Enhanced radio-SLAM with Bayesian MLE and developed a GraphSLAM framework for multi-epoch smoothing. Validated robust localization/mapping using real 60-GHz measurements.

DC-10 – Alexandra Chatzicharistou (UCL-1):
Derived closed-form ISL–constellation relations and performed Pareto-optimal constellation shaping. Experimentally validated sensing–communication trade-offs via SDR tests.

DC-11 – Arya Kanathil Meethal (WUP-1):
Designed and simulated a 5.9-GHz SMRS leaky-wave antenna for vehicular ISAC. Demonstrated beam steering (30°→43°) and established the basis for varactor-tunable reconfigurable designs.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

The project started on January 1, 2024. So far, two conference papers have been published:
1) X. Zhang, O. Kaltiokallio, Y. Ge, H. Wymeersch and M. Valkama, “Robust and Efficient Bayesian Approach for Snapshot Radio SLAM,” in Proceedings of 2025 International Conference on Localization and GNSS (ICL GNSS), June 10-12, 2025, Rome, Italy, pp. 1–7.
2) Y. Zhang, G. Pan, M. F. Keskin, O. Kaltiokallio, M. Valkama, and H. Wymeersch, “UNILoc: Unified localization combining model-based geometry and unsupervised learning,” in Proceedings of 2025 IEEE Global Communications Conference (GLOBECOM), Dec. 8-12, 2025, Taipei, Taiwan, pp. 1–6.

Many more results are expected in the next reporting period.

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