多用户多目标场景下的通信感知一体化加权优化波束形成算法

Objective The increasing demand for wireless communication has led to a significant scarcity of spectrum resources, while the inherent coupling between communication and sensing systems allows for shared spectrum utilization. Integrated Sensing and Communication (ISAC) thus shows considerable promise for future applications. However, most existing studies focus on optimizing either communication or sensing performance, treating the other as a constraint, which limits system flexibility. This approach becomes particularly problematic in complex multi-user, multi-target scenarios, where balancing both functionalities is essential. Additionally, previous works often assume symmetrically distributed radar targets in small quantities, simplifying optimization but diverging from practical asymmetric and dense target distributions. To address these limitations, this study explores ISAC systems with asymmetric multi-target configurations, aiming to improve flexibility and practicality through joint optimization of communication and sensing performance optimization. Methods This study adopts an MIMO radar framework in which orthogonal transmit signals maximize waveform Degrees of Freedom (DoFs) in proportion to the antenna count. A beamforming matrix is designed to detect targets across multiple directions while allocating distinct waveforms for communication and sensing tasks. In contrast to conventional antenna configurations, the proposed scheme utilizes all antennas for radar detection, enhancing sensing performance. To address the limitations of single-objective optimization, a Pareto optimization framework is introduced, allowing for weighted trade-offs between the communication Weighted Sum Rate (WSR) and radar beam pattern error. This framework is adaptable to dynamic scenarios. To handle the non-convexity of the optimization problem, a hybrid algorithm combining Weighted Minimum Mean Square Error (WMMSE) and Semidefinite Relaxation (SDR) is proposed. Specifically, the WSR and radar error maximization problem is first reformulated as a Mean Square Error (MSE) minimization problem, followed by SDR-based relaxation of constraints for tractable solutions. Results and Discussions As shown in (Fig. 2, Fig. 3): (a) The proposed beamforming design demonstrates superior flexibility compared to single-objective optimization, enabling adaptable balancing of communication and sensing performance across scenarios by adjusting the Pareto weight factor. (b) Compared to separated deployment schemes, the proposed method utilizes more antennas for sensing, concentrating transmit power in specific directions to enhance target detection capability. (c) With comparable radar performance, the communication WSR of the proposed scheme shows an 11.6% improvement over shared deployment configurations.(Fig. 4) further illustrates the radar detection error under varying SNRs. Regardless of the performance weight values, the radar detection error decreases with increasing transmit power, indicating that higher power improves system performance. Under constant transmit power, a smaller performance weight results in higher radar detection accuracy, as more power is allocated to radar performance optimization. For a more comprehensive comparison, (Fig. 5) shows the beamforming patterns under different transmit power levels for the separated deployment scheme. In this scheme, as transmit power increases, the radar detection error actually increases. This occurs because the system optimizes detection performance in a specific direction, achieving optimal precision there. As shown in the figure, as power increases, the antenna power becomes concentrated in the direction of the target at 0°, significantly improving resolution in that direction, while detection performance in other directions deteriorates. This indicates that the separated deployment scheme is limited in its ability to meet detection requirements for multiple targets simultaneously. (Fig. 6) demonstrates that, at all transmit power levels, the proposed scheme exhibits clear advantages in communication performance. (Fig. 8, Fig. 9) analyze the impact of target quantity on radar detection error, confirming robustness in multi-target asymmetric scenarios. When transmit power and weight factors remain unchanged, increasing the number of radar detection targets leads to an increase in radar detection error. This happens because total power remains constant, and adding more detection targets reduces the power allocated to each target. This effect becomes more pronounced as the number of targets grows. However, this error increase can be mitigated by increasing transmit power. Simulation results show that the proposed scheme consistently outperforms other methods under different target numbers, demonstrating its ability to efficiently utilize limited power and maintain low detection errors, even as the number of targets increases. (Fig. 10) reveals directional limitations, as beam patterns at edge angles exhibit weak directivity, complicating peripheral target detection. Algorithm convergence curves (Fig. 11) and Pareto frontiers for communication-radar trade-offs (Fig. 12) confirm the stability and flexibility of the proposed scheme. Conclusions This study addresses the limitations of single-objective optimization (communication or radar performance) and constrained radar degrees of freedom by proposing a weighted joint beamforming design for ISAC. The ground base station, equipped with dual functionalities, optimizes the WSR and radar beam pattern error. By adjusting the Pareto weight factor, flexible performance trade-offs between communication and sensing are achieved, improving adaptability to diverse scenarios. Experimental results demonstrate that, under optimized weights and transmit SNR, the proposed scheme reduces radar detection error by 36.2% and enhances communication SINR by 1 dB compared to separated deployment strategies. These advancements validate the effectiveness of the joint optimization framework in practical asymmetric multi-target environments, providing a robust foundation for next-generation ISAC systems.