SINR Adaptive Symbol Level Precoding and Position Joint Optimization Strategy for Multiple Unmanned Aerial Vehicles Anti-Jamming Communication

WEI Haoran; YAO Rugui; FAN Ye; MA Weixin; ZUO Xiaoya

doi:10.11999/JEIT250221

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WEI Haoran, YAO Rugui, FAN Ye, MA Weixin, ZUO Xiaoya. SINR Adaptive Symbol Level Precoding and Position Joint Optimization Strategy for Multiple Unmanned Aerial Vehicles Anti-Jamming Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250221

Citation:

WEI Haoran, YAO Rugui, FAN Ye, MA Weixin, ZUO Xiaoya. SINR Adaptive Symbol Level Precoding and Position Joint Optimization Strategy for Multiple Unmanned Aerial Vehicles Anti-Jamming Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250221

Citation:

WEI Haoran, YAO Rugui, FAN Ye, MA Weixin, ZUO Xiaoya. SINR Adaptive Symbol Level Precoding and Position Joint Optimization Strategy for Multiple Unmanned Aerial Vehicles Anti-Jamming Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250221

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SINR Adaptive Symbol Level Precoding and Position Joint Optimization Strategy for Multiple Unmanned Aerial Vehicles Anti-Jamming Communication

doi: 10.11999/JEIT250221 cstr: 32379.14.JEIT250221

WEI Haoran¹,
YAO Rugui¹,
FAN Ye^{1, 2
,
,},
MA Weixin¹,
ZUO Xiaoya¹

1.
College of Electronic Information, Northwestern Polytechnical University, Xi’an 710129, China
2.
Northwestern Polytechnical University Shenzhen Research Institute, Shenzhen 518087, China

Funds: The National Natural Science Foundation of China (62401473). The National Key Laboratory Fund Project for Space Microwave Communication (HTKJ2024KL504010). Shenzhen Science and Technology Program (JCYJ20240813150735045). The Key R&D Project in Shaanxi Province (2025CY-YBXM-055). The National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU(WR202404), The Fundamental Research Funds for the Central Universities (G2024WD0159, D5000240239)

Received Date: 2025-04-01
Rev Recd Date: 2025-07-02

Available Online: 2025-07-07

Abstract

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 and 7), 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.

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References(18)

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