SINR Adaptive Symbol Level Precoding and Position Joint Optimization Strategy for Multiple Unmanned Aerial Vehicles Anti-Jamming Communication
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摘要: 无人机部署为空中通信节点可为地面用户提供更为灵活、更高质量的服务。然而,无人机易受到外界恶意干扰导致通信性能严重下降。传统抗干扰方案如跳频抗干扰计算量大,在计算资源与能量受限的无人机上难以适用。针对上述问题,该文综合采用多点协作(CoMP)技术和符号级预编码(SLP)技术,提出多无人机信干噪比适配的符号级预编码与位置联合抗干扰优化策略。针对三维空间和存在同一节点属于多个集合等挑战,巧妙地设计了3D-Kmeans++协作集划分的空地多节点匹配机制。为了解决预编码矩阵和位置联合优化时计算量大的问题,基于最优预编码矩阵和位置之间的关联关系,提出基于粒子重构的低复杂度迭代优化算法,可同时求解出无人机的预编码矩阵和位置。另外,针对用户所受干扰和噪声的差异化功率特征,提出信干噪比(SINR)适配的符号级预编码,实现了多架无人机功率分配的优化设计。仿真结果表明,与不划分协作集对比,采用该文所提空地多节点匹配机制后受干扰通信用户的信干噪比提升5 dB左右;与传统符号级预编码对比,采用该文所提预编码和迭代优化算法,受干扰通信用户的信干噪比能提升12~13 dB,同时保证普通通信用户的正常通信不受影响,提升了系统能量效率,降低了计算复杂度。
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关键词:
- 无人机部署 /
- 3D-Kmeans++协作集划分 /
- 粒子重构 /
- 信干噪比适配的符号级预编码
Abstract:Objective Unmanned Aerial Vehicles (UAVs) are widely applied in areas such as traffic monitoring, wireless coverage, and precision agriculture due to their high mobility and deployment flexibility. In air–ground communication scenarios requiring flexible deployment, UAV mobility can be leveraged to counteract external malicious jamming. Further, the collaborative operation of multiple UAVs enables improved system performance. However, the broadcast nature of wireless communication renders multiple UAV communication systems vulnerable to jamming attacks that disrupt legitimate communication services. Addressing this challenge, this study proposes a Signal-to-Interference-plus-Noise Ratio (SINR) adaptive Symbol-Level Precoding (SLP) and position joint optimization strategy for anti-jamming communication in multi-UAV systems. The approach fully exploits UAV mobility to enhance communication robustness under different user requirements. By integrating Coordinated Multi-Point (CoMP) transmission with SLP in a Multi-User Multiple-Input Single-Output (MU-MISO) system, the strategy improves interference utilization, enhances system energy efficiency, and reduces computational complexity. Methods An SINR adaptive SLP and position joint anti-jamming optimization strategy for multiple UAVs is proposed by integrating CoMP and SLP technologies. To address the challenges of three-dimensional operational space and the overlap of nodes assigned to multiple sets, a ground-to-air multi-node matching mechanism based on three-dimensional K-means++ collaborative set partitioning is designed. To reduce the computational complexity of the joint optimization process, an iterative optimization algorithm based on particle reconstruction is developed. This algorithm simultaneously solves for both the UAV precoding matrix and spatial positions with low computational overhead. Additionally, an SINR adaptive SLP approach is introduced to enable optimized power allocation for multiple UAVs, considering the varying power characteristics of jamming and noise experienced by users. Simulation results demonstrate that the integration of CoMP and SLP technologies effectively enhances the communication performance of jammed users, while stable communication performance is maintained for ordinary users. Results and Discussions In the proposed UAV anti-jamming communication strategy, the SINR of jammed users is improved without compromising the normal communication performance of ordinary users. In the simulation results, UAV positions are marked with five-pointed stars ( Figs. 6 and7 ), UAV coverage areas are represented by circles, and users are indicated by “*”. A comparison of SINR variations under four schemes (Fig. 8 ) shows that the received SINR of jammed users increases by approximately 12~13 dB, while the SINR of ordinary users remains above the required threshold. When different SINR thresholds are applied, the received SINR of each user type varies accordingly (Fig. 9 ). By setting appropriate thresholds based on actual scenario requirements, different energy allocation effects can be achieved. Following optimization, the Bit Error Rate (BER) of jammed users is significantly reduced (Fig. 10 ). The constellation diagrams comparing the received signals under two precoding schemes (Fig. 11 ) indicate that the proposed SINR adaptive SLP strategy for multiple UAVs effectively improves the SINR of jammed users, while maintaining the communication quality of ordinary users. Moreover, the fitness evolution curve of the iterative optimization algorithm based on particle reconstruction (Fig. 12 ) shows that the algorithm approaches the global optimal solution at an early stage of iteration.Conclusions To address the challenge of anti-jamming communication for multi-user services supported by multiple UAVs, this study integrates CoMP transmission with SLP technology and proposes an SINR adaptive SLP and position joint optimization strategy for multi-UAV anti-jamming communication. The strategy is implemented in two stages. First, to solve the clustering problem in three-dimensional space and allow nodes to belong to multiple groups, a ground-to-air multi-node matching mechanism based on three-dimensional K-means++ collaborative set partitioning is designed. Second, an SINR adaptive SLP method is proposed to optimize UAV power allocation based on the SINR requirements of different user types. To reduce the computational complexity of jointly optimizing the precoding matrix and UAV positions, an iterative optimization algorithm based on particle reconstruction is developed. Simulation results demonstrate that the proposed strategy, by combining CoMP and SLP, effectively improves the communication performance of jammed users while maintaining reliable communication for ordinary users. -
表 1 仿真参数
名称 符号 数值 用户数量 $ N $ 10 无人机数量 $ M $ 3 协作无人机数量 $ {N_x} $ 2 无人机发送信号总功率(W) $ P $ 1 无人机飞行高度范围(m) $ \left[ {{H_{\min }},{H_{\max }}} \right] $ [400, 600] 无人机飞行水平范围(m) $ \left[ {{x_{\lim }},{y_{\lim }}} \right] $ [250, 250] 无人机覆盖半径与高度的比值 $ \tan \theta /2 $ 1 信干噪比阈值(dB) $ {{\mathrm{SINR}}_\gamma } $ 3 高斯白噪声功率(W) $ {N_0} $ $ {10^{ - 10}} $ -
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