Research on the Optimization Method of Low Earth Orbit Integrated Sensing and Communication Based on Multi-Dimensional Resource Joint Scheduling
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摘要: 针对低轨卫星场景下，传统固定功率分配、频谱带宽受限及全双工同频干扰等影响通感一体化性能的问题，该文提出一种基于多维资源联合调度的通感一体化(ISAC)性能优化方法。首先，以通信可达和速率、雷达探测互信息和位置精度因子的综合性能为优化目标，并考虑低轨卫星星座同时可用卫星数量多的特点，建立了包含选星、子信道功能分配和功率分配的多变量联合优化问题；其次，针对该混合整数非线性规划问题难以直接求解的特点，将该问题解耦成子问题，并基于块坐标下降法设计了一个综合性能优化方法对所解耦的子问题进行求解；最后，仿真结果表明，与基准方案相比，所提方法在相同资源限制下，提高用户的通信与感知综合性能可达7%以上，并探讨了较优的协作卫星数量。Abstract:
Objective With the rapid development of Low Earth Orbit (LEO) satellite constellations and Integrated Sensing And Communication (ISAC) systems, performance optimization faces increasing challenges due to fixed power distribution, spectrum limitations, and interference between communication and sensing functions. This study proposes an optimization method based on multi-dimensional resource joint scheduling to address these constraints in LEO satellite environments. The method enhances the combined performance of communication and sensing by leveraging the high satellite visibility of LEO constellations. The optimization focuses on improving communication reach, data rate, radar mutual information, and positioning accuracy while ensuring efficient resource allocation. Methods The optimization problem is formulated as a multi-variable joint problem, incorporating satellite selection, subchannel function allocation, and power distribution. To address the complexity of this Mixed-Integer NonLinear Programming (MINLP) problem, it is decoupled into subproblems and solved iteratively using the Block Coordinate Descent (BCD) method. Satellite selection is optimized using a modified Multi-Population Genetic Algorithm (MPGA), which accounts for communication link quality, sensing capabilities, and satellite geometric distribution. Subchannel allocation and power distribution are iteratively optimized to maximize system performance while maintaining a balance between communication and sensing tasks. Results and Discussions The proposed optimization method is evaluated through simulations against benchmark schemes. Results indicate that, under the same resource constraints, the method enhances integrated communication and sensing performance by over 7% ( Fig. 5 ). Improvements are observed in communication efficiency, radar detection mutual information, and positioning accuracy. Additionally, the number of cooperating satellites significantly affects system performance, though gains diminish beyond an optimal threshold (Fig. 4 ). This highlights the importance of strategic satellite selection and coordination to balance performance gains with complexity and resource usage. Moreover, the results confirm the convergence of the proposed method, demonstrating consistent performance across multiple scenarios (Fig. 3 ).Conclusions This study proposes an optimization approach for ISAC systems in LEO satellite constellations, addressing challenges related to resource allocation, power distribution, and interference management. The multi-dimensional resource joint scheduling method enhances overall system performance by optimizing satellite selection, subchannel allocation, and power distribution. Simulation results demonstrate that: (1) The proposed optimization method improves integrated communication and sensing performance in LEO satellite ISAC systems, achieving a performance gain of over 7% compared to benchmark solutions. (2) The multi-dimensional resource joint scheduling approach effectively balances communication and sensing tasks by optimizing satellite selection, subchannel function allocation, and power distribution, thereby mitigating interference and resource constraints. (3) The number of cooperating satellites significantly influences system performance. However, beyond an optimal threshold, additional satellites yield diminishing returns, emphasizing the need for efficient satellite coordination. This study assumes ideal sensing capabilities; future research should incorporate real-world constraints, such as satellite mobility and environmental factors, to enhance the practical applicability of the proposed approach. -
1 选星子算法
输入：${{s}}$={${S_1}$, ${S_2}$, ···, ${S_T}$}, $ {\boldsymbol{\rho}} $, ${\boldsymbol{G}}$, J； (1) 初始化不同种群，进行二进制编码随机生成个体； (2) 分别计算不同种群的适应度； (3) 每隔J代，不同种群共享信息，形成各种群交配池； (4) 自适应调整交叉和变异率，执行交叉与变异操作生成子代； (5) 计算适应度，将各种群最优个体引入精英种群； (6) 重复步骤(2)～(5)，直到达到最大代数或收敛条件； 输出：主星${S_{\text{M}}}$和相应的辅助星集合${S_{\text{A}}}$； 
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2 子信道与功率分配
输入：迭代次数${\text{it}}$，信道功率$ {\boldsymbol{P}} $，信道分配矩阵$ {\boldsymbol{\rho}} $，子信道增益
矩阵${{\boldsymbol{H}}_{{\text{com}}}}$, ${{\boldsymbol{H}}_{{\text{sense}}}}$，迭代终止阈值$\varepsilon $(1) 初始化信道功率矩阵$ {\boldsymbol{P}} $，信道分配矩阵$ {\boldsymbol{\rho}} $，设置迭代次数
${\text{it = 0}}$；(2) repeat (3) 通过式(17)、式(18)更新辅助变量${\boldsymbol{a}}$和${\boldsymbol{b}}$； (4) 通过求解器更新$ {\boldsymbol{\rho}} $； (5) 通过式更新$ {\boldsymbol{P}} $； (6) 计算$ {S^{{\text{it}}}}({\boldsymbol{\rho}} ,P) $ (7) ${\text{it = it + 1}}$； (8) until $\dfrac{{|{S^{{\text{it}}}}({\boldsymbol{\rho }},{\boldsymbol{P}}) - {S^{{{{\mathrm{it}} - 1}}}}({\boldsymbol{\rho}} ,{\boldsymbol{P}})|}}{{{S^{{\mathrm{it}} - 1}}({\boldsymbol{\rho}} ,{\boldsymbol{P}})}} \le \varepsilon $； 输出：信道功率矩阵$ {\boldsymbol{P}} $，信道分配矩阵$ {\boldsymbol{\rho}} $；
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3 LEO-ISAC联合资源分配策略
输入：星座坐标，搜索区域,${P_{\max }}$, ${P_{{\text{total}}}}$, ${\text{PDO}}{{\text{P}}_{\min }}$，子信道增益矩阵${{\boldsymbol{H}}_{{\text{com}}}}$, ${{\boldsymbol{H}}_{{\text{sense}}}}$，迭代终止阈值${\boldsymbol{\mu }}$ (1)初始化：排除不符合基础条件的星，剩余星获得初始子信道分配矩阵${\boldsymbol{ \rho}} $； 根据信道分配结果，得到初始功率分配矩阵$ {\boldsymbol{P}} $。设置迭代次
数${\text{it = 0}}$；(2)通过算法1获得选星结果； (3)固定选星，通过算法2优化子信道和功率分配； (4)更新$s$, $ {\boldsymbol{\rho }}$, $ {\boldsymbol{P}} $； (5)终止：当差值小于$\mu $或达到最大迭代次数时，结束；否则，返回步骤(2)继续循环。通过算法1获得选星结果； 输出：选星集合$s$，信道功率矩阵$ {\boldsymbol{P}} $，信道分配矩阵$ {\boldsymbol{\rho }}$；
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