Integrated Sensing and Communication Beamforming Design With Target Model Aware Antenna Selection
IEEE Internet of Things Journal, cilt.13, sa.1, ss.963-977, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 13 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/jiot.2025.3626821
- Dergi Adı: IEEE Internet of Things Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Compendex, INSPEC
- Sayfa Sayıları: ss.963-977
- Anahtar Kelimeler: Beamforming optimization, Cramér–Rao bound (CRB), energy efficiency, extended target (ET), extra-large antenna arrays, far-field, integrated sensing and communication (ISAC), near field, point target (PT)
- İstanbul Medipol Üniversitesi Adresli: Evet
Özet
For high-resolution sensing in integrated sensing and communication (ISAC) systems, the deployment of extra-large antenna arrays (XLAAs) is essential. This, however, renders the traditional point target (PT) model inaccurate. Instead, targets must be considered as having a spatial extent over range and angle, necessitating their modeling as extended targets (ET) for accurate sensing, especially within the near-field propagation region. This shift to ET modeling often entails a significant increase in energy consumption, and reduced sum-rate and increased latency for the communication users compared to the simpler PT model. To address this critical tradeoff, this article proposes an antenna selection strategy for XLAA-based ISAC. By selectively activating antenna elements, the proposed ISAC design aims to maintain effective far-field PT operating conditions, thereby enhancing energy efficiency and communication sum-rate. The optimization ensures the communication quality-of-service by enforcing signal-to-interference-plus-noise power ratio constraints for the communication users, while inherently managing the sensing performance evaluated via the Cramér-Rao bound. This strategy provides a controllable operating point, balancing the ET model’s high sensing accuracy, which comes with higher signal processing time and lower communication sum-rate, against the PT model’s lower sensing accuracy but lower processing time and higher sum-rate. Numerical results validate the proposed approach, demonstrating substantial improvements in energy efficiency and sum-rate over pure ET modeling, achieved at a quantifiable cost in sensing accuracy.