Robust Beamforming Design Under Statistical Channel Knowledge for Multi-User Radar Communication Systems

Abstract

The simultaneous operation of radar and communication systems over the same frequency band causes severe mutual interference, especially in multi-user multiple-input multiple-output (MIMO) scenarios. Most existing radar–communication coexistence schemes rely on instantaneous channel state information, which is difficult to acquire accurately in fast-varying environments and leads to high signaling overhead and computational complexity. This paper investigates a robust joint beamforming framework for multi-user MIMO radar–communication coexistence systems based exclusively on statistical channel state information. The objective is to improve the achievable ergodic sum rate of communication users while preserving radar operational requirements under transmit power and coexistence constraints. By exploiting long-term channel statistics and large-system analysis, a deterministic approximation of the ergodic sum rate is derived, enabling low-complexity beamforming design without requiring instantaneous channel knowledge. To address imperfections in statistical CSI estimation, a worst-case robust optimization framework is developed, and an efficient alternating optimization algorithm is proposed. Simulation results demonstrate that the proposed robust beamforming scheme significantly outperforms non-robust statistical-CSI-based approaches, achieves performance close to instantaneous-CSI-based benchmarks, and maintains robustness against statistical CSI uncertainty while effectively managing the trade-off between communication performance and radar interference.