Waveform Design of UAV-Enabled Integrated Sensing and Communication in Marine Environment

Objective In the future, 6G will usher in a new era of intelligent interconnection and the integration of virtual and physical environments. This vision relies heavily on the deployment of numerous communication and sensing devices. However, the scarcity of frequency resources presents a significant challenge for sharing these resources efficiently. Integrated Sensing and Communication (ISAC) technology offers a promising solution, enabling both communication and sensing to share a common set of equipment and frequency resources. This allows for simultaneous target detection and information transmission, positioning ISAC as a key technology for 6G. ISAC research can be divided into two main approaches: Coexisting Radar and Communication (CRC) and Dual-Function Radar Communication (DFRC). The CRC approach designs separate systems for radar and communication, aiming to reduce interference between the two; however, this leads to increased system complexity. The DFRC approach integrates radar and communication into a single system, simplifying the design while still achieving both radar detection and communication functions. As a result, DFRC is the primary focus of ISAC research. Waveform design is a crucial component of ISAC systems, with two primary strategies: non-overlapped resource waveform design and fully unified waveform design. The fully unified design can be further classified into three types: sensing-centric, communication-centric, and joint design. Previous research has predominantly focused on sensing-centric or communication-centric designs, which limit the flexibility of the integrated waveform in balancing communication and sensing performance. Additionally, limited research has addressed ISAC in marine environments. This paper investigates waveform design for ISAC in marine environments, proposing a joint design approach that uses a weighting coefficient to adjust the communication and sensing performance of the integrated waveform. Methods Considering the characteristics of the marine environment, this paper proposes using Unmanned Aerial Vehicles (UAVs) as nodes in the ISAC system, owing to their flexibility, portability, and cost-effectiveness. The integrated waveform transmitted by UAVs can both communicate with downlink users and detect targets. The communication performance is evaluated using the achievable sum rate, while the sensing performance is assessed by the error between the covariance matrix of the integrated waveform and the standard radar covariance matrix. The optimization objective is to maximize the weighted sum of these two performance indices, subject to the constraint that UAV power does not exceed the maximum allowable value. The weighting coefficient represents the ratio of communication power to sensing power. Due to the non-convex rank-1 constraint and objective function, the optimization problem is non-convex. This paper decomposes the non-convex optimization problem into a series of convex subproblems using the Successive Convex Approximation (SCA) algorithm. The local optimal solution of the original problem is obtained by solving these convex subproblems. The communication and sensing performance of the integrated waveform can be adjusted by varying the weighting coefficient. The performance of the weighted integrated waveform design in a marine environment is simulated, and the results are presented. Results and Discussions Simulation results indicate that the integrated beam pattern exhibits two large lobes: one directed towards the target for detection, and the other towards the communication user (Fig.4). As the weighting coefficient increases, the lobes directed towards the communication users become more pronounced, reflecting the increased emphasis on communication performance. Furthermore, as the weighting coefficient increases, the sensing performance error (smaller error indicates better sensing performance) initially increases slowly before rising more rapidly. Meanwhile, the achievable sum rate of communication increases sharply. Eventually, both the sensing performance error and the communication sum rate curves flatten out (Fig. 6). Since the UAV's maximum power is limited to 10 W, further increases in the weighting coefficient beyond a certain point lead to diminishing returns in communication performance, as power constraints limit further improvement. At this point, the sensing performance error remains stable. Conclusions This paper investigates the waveform design for UAV-enabled ISAC systems in marine environments. A wireless propagation model for UAVs in such environments is developed, and an integrated waveform optimization method based on a weighted design is proposed. The SCA algorithm is used to solve the convex approximation. Simulation results demonstrate that when the weighting coefficient is between 0.2 and 0.5, the integrated waveform ensures strong communication performance while maintaining good sensing performance.