Robust ISAC based framework for location estimation and target detection in 6G networks

To enhance spectrum utilization and situational awareness in sixth generation (6G) networks, integrated sensing and integration (ISAC) is introduced as a unified functionality. This paper proposes a centralized ISAC based framework operating within a Cloud-Radio Access Network (C-RAN) architecture. The system employs multiple transmit and receive access points equipped with uniform linear antenna arrays to enable simultaneous communication and high-resolution environmental sensing. A hybrid signal transmission model is developed, incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) channels under realistic propagation conditions. Time of Arrival (TOA), Time Difference of Arrival (TDOA), and Direction of Arrival (DOA) techniques are implemented for cooperative localization, while radar-based target detection is analyzed using hypothesis testing. The localization mean square error (MSE) and probability of detection (PD) are evaluated for different number of receivers M and number of observation samples L under varied signal-to-noise ratio (SNR) values. It is observed that a gain of 8.75dB is achieved at SNR of 10dB with DOA estimation as the value of M is changed from 5 to 10. Also, the PD improves with increasing M and L offering a gain of 15 dB with Swerling model-1 and 20 dB with Swerling model-2. The impact of noise standard deviation [Formula: see text] and [Formula: see text] on the estimation accuracy is also presented. In the end, it is shown that the proposed ISAC framework offers scalable solutions for 6G IoT networks and autonomous systems with enhanced localization accuracy and detection reliability.