Integrated sensing and communication (ISAC) emerges as a transformative paradigm for enabling future wireless networks by jointly supporting high-rate communication and precise environmental perception. In this paper, we investigate a robust beamforming framework tailored for monostatic ISAC systems in near-field channels, where the impact of spherical wavefront effects is significant and cannot be neglected. In particular, we formulate a minimum mean squared error (MMSE)-driven beamforming optimization problem that strikes an effective balance between sensing accuracy and communication quality, while considering both power and outage constraints and explicitly accounting for channel estimation errors. To address the inherent non-convexity arising from coupled sensing-communication constraints and uncertainties due to channel errors, a semidefinite relaxation (SDR)-based algorithm leveraging sphere bounding techniques is developed to acquire a favorable suboptimal solution, with polynomial-time complexity. Furthermore, extensive simulations are conducted to rigorously validate the proposed methodology under a variety of practical conditions. Our results demonstrate that the proposed design reduces sensing mean squared error (MSE) by up to 3.8 dB and improves achievable communication rate by 33.4% over non-robust baselines under imperfect channel state information (CSI). Furthermore, compared to conventional far-field schemes, our design achieves up to 1.6 dB lower MSE and 28.0% higher rate in millimeter-wave (mmWave) scenarios, and 2.5 dB lower MSE and 31.7% rate gain in terahertz (THz) scenarios at 10 dB communication signal-to-interference-plus-noise ratio (SINR). To further underscore the practical significance of our approach, the experimental results reveal critical insights into the delicate balance between communication and sensing performance in ISAC systems under real-world conditions. Specifically, our findings emphasize the importance of robust beamforming techniques that optimize resource allocation to mitigate the adverse effects of channel estimation errors, particularly in high-frequency regimes such as mmWave and THz. These insights are vital for the design of adaptive ISAC systems, where trade-offs between sensing precision and communication reliability must be dynamically managed to meet stringent performance requirements in next-generation wireless networks.

Integrated Sensing and Communication (ISAC) systems represent a transformative paradigm for next-generation wireless networks by enabling dual-functional efficiency through simultaneous information transmission and environmental sensing. This paper investigates the critical challenge of robust beamforming design for monostatic ISAC systems operating in the near-field (NF) regime, where conventional far-field channel assumptions become fundamentally invalid. We develop a novel robust beamforming framework that optimizes minimum mean squared error (MMSE) estimation for sensing performance while guaranteeing stringent communication quality-of-service requirements. A distinctive feature of our approach lies in the proposed spherical wavefront-based channel model that incorporates both distance and angular response, providing superior accuracy compared to conventional planar wavefront approximations in NF scenarios. To resolve the inherent non-convex optimization problem with coupled sensing-communication constraints, we devise an efficient semidefinite relaxation (SDR)-based algorithm with guaranteed convergence properties. Comprehensive simulations demonstrate significant improvements in both sensing and communication performance, even under imperfect channel state information.