A Novel SLP Approach for Enhancing 6G RIS-Assisted DFRC UAV Networks

The advancement of 6G technology has highlighted the importance of integrating communication and sensing networks to address challenges in urban environments with non-line-of-sight (NLOS) conditions. This paper proposes a novel framework leveraging Reconfigurable Intelligent Surfaces (RIS) and Symbol-Level Precoding (SLP) to enhance Dual-Function Radar Communication (DFRC) systems for UAV applications. By introducing an Alternating Dual Optimization (ADO) algorithm, we jointly optimize transmission waveforms and RIS reflection coefficients, enabling precise target detection and efficient multi-user communication. Simulations demonstrate significant improvements, including a two-order reduction in Symbol Error Rate (SER) and a 78.86% decrease in base station (BS) transmission energy compared to conventional methods. These findings underscore the potential of RIS assisted DFRC systems to advance UAV communication and sensing capabilities in 6G-enabled urban scenarios.