Cell-Free Joint Beamforming and AP–User/Target AssociationOptimization for Integrated Sensing and Communication
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摘要: 通信感知一体化(ISAC)是未来6G的关键技术场景。区别于现有小区体制，基于Cell-free架构的ISAC系统需要对接入点(AP)的波束成形与AP-用户/目标匹配进行联合优化。针对上述问题，该文提出一种基于二进制无线电地图(BRM)的联合通信感知一体化波束成形与AP匹配优化方法。首先，利用BRM提供的环境信息对AP与用户/目标之间的信道进行预测，提供AP-用户/目标匹配所需的全局信道信息。在此基础上，建立基于ISAC满意度的优化模型，进而借助遗传算法设计了AP波束成形与AP-用户/目标匹配的迭代优化算法。仿真表明，相比于现有方案，所提方法能够有效提升Cell-free架构下系统的通信感知一体化性能。
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关键词:
- Cell-free架构 /
- 通信感知一体化 /
- 波束成形 /
- 接入点-用户/目标匹配 /
- 二进制无线电地图
Abstract:Objective Integrated Sensing And Communication (ISAC) is a key technology for Sixth-Generation (6G) networks. The cell-free architecture is a promising regional coverage paradigm for 6G. Cooperation among Access Points (APs) mitigates coverage imbalance, interference, and capacity limitations in conventional cellular systems, while enabling communication and sensing services for low-altitude targets with wide-area continuous coverage. However, existing studies on cell-free systems often rely on statistical channel models, which fail to capture realistic propagation characteristics in complex environments. The global Channel State Information (CSI) required for transmission optimization is difficult to obtain, and instantaneous CSI cannot be guaranteed due to the high mobility of low-altitude targets. To address these issues, a joint beamforming and AP–user/target association optimization method based on a Binary Radio Map (BRM) is proposed. The environmental information provided by the BRM is used to predict channels between APs and users/targets, thereby providing global channel information for joint optimization. On this basis, an ISAC satisfaction-based optimization model is constructed, and an iterative optimization algorithm for beamforming design and AP–user/target association is developed using a genetic algorithm. Methods First, the channels between APs and users/targets are predicted using environmental information derived from the BRM. An ISAC satisfaction-based optimization model is then established to unify communication and sensing performance. Due to the coupling between communication and sensing and the non-convex nature of the problem, the optimization problem is decomposed into two subproblems corresponding to communication and sensing beamforming. In each iteration, the beamforming design is reformulated as a Second-Order Cone Program (SOCP) to obtain beamforming matrices that maximize the satisfaction function. An iterative solution algorithm is applied to compute the communication and sensing beamforming matrices efficiently. Subsequently, based on the optimized satisfaction function, an AP–user/target association optimization method is designed using a genetic algorithm. Results and Discussions Simulation results verify the effectiveness of the BRM-assisted channel prediction and association optimization method. Compared with the conventional AP association method based on the shortest path, the proposed approach reduces the required transmission power by approximately 5 dBm while achieving higher user/target satisfaction ( Fig. 7 ). As the transmission power increases, the satisfaction of users/targets gradually improves and approaches 1. In contrast, under the conventional scheme, a large gap remains between the maximum and minimum satisfaction values at the same transmission power (Fig. 8 ). When the transmission power is 40 dBm, the proposed method effectively reduces this disparity and balances performance among different users/targets. Although the null-space projection scheme leads to some degradation in sensing performance, the minimum received sensing power remains stable. This indicates that the overall system satisfaction is not affected and that sensing requirements are still satisfied (Fig. 9 ).Conclusions This study addresses the AP-user/target association problem in low-altitude airspace. The BRM is used to predict channels between APs and users/targets and to provide global channel information for joint optimization. By maximizing the minimum user/target satisfaction, ISAC beamforming is optimized, and AP-user/target association is iteratively refined using a genetic algorithm. Simulation results show that the proposed method effectively improves AP-user/target association and enhances integrated communication and sensing performance compared with existing approaches. -
1 问题$ {\mathcal{P}}_{2} $的求解算法
输入：初始化迭代精度$ {\varepsilon }_{0} $, $ {\varepsilon }_{1} $，最大发射功率$ {P}_{{m}}^{m\mathrm{ax}} $，CU满意
度范围$ {\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right)\in \left[\mathcal{F}_{\mathrm{C}}^{\mathrm{min}},\mathcal{F}_{\mathrm{C}}^{\mathrm{max}}\right] $，ST满意度范围$ {\mathcal{F}}_{{v}}\left({\boldsymbol{b}}_{\mathcal{D}}\right)\in $
$ \left[\mathcal{F}_{\mathrm{S}}^{\mathrm{min}},\mathcal{F}_{\mathrm{S}}^{\mathrm{max}}\right] $，通信与感知传输链路$ {\boldsymbol{h}}_{{mk}} $，噪声方差$ {\sigma }^{2} $。输出：最佳通信波束$ {\boldsymbol{W}}_{{m}} $；最佳功率分配因子$ {\alpha }_{{mv}} $；综合满意
度$ \mathcal{F}\left({\boldsymbol{b}}_{\mathcal{D}}\right)=\mathrm{min}\left\{{\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{{{\mathcal{D}}_{{u}}}}\right),{\mathcal{F}}_{{v}}\left({\boldsymbol{b}}_{{{\mathcal{D}}_{{v}}}}\right)\right\} $。(1) WHILE$ \left| {\mathcal{F}}_{{v}}\left({\boldsymbol{b}}_{\mathcal{D}}\right)-{\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right)\right| \lt {\varepsilon }_{0} $DO： (2) 令$ {\mathcal{F}}_{{v}}\left({\boldsymbol{b}}_{\mathcal{D}}\right)=\dfrac{1}{2}\left(\mathcal{F}_{\mathrm{C}}^{\mathrm{min}}+\mathcal{F}_{\mathrm{C}}^{\mathrm{max}}\right) $； (3) 解决通信优化问题$ {\tilde{\mathcal{P}}}_{2.1} $中，满意度$ {\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right) $约束下的凸问
题，并得到最佳通信功率$ P_{m,\mathrm{C}} $；(4) 若$ P_{m,\mathrm{C}}\ge P_m^{m\mathrm{ax}} $，令$ \mathcal{F}_{\mathrm{C}}^{\mathrm{max}}={\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right) $并执行步骤(5)；否
则，令$ P_{m,\mathrm{S}}=P_m^{m\mathrm{ax}}-P_{m,\mathrm{C}} $；(5) 由满意度效用函数(11)映射得到$ {\mathcal{T}}_{{v}}\left({\boldsymbol{b}}_{\mathcal{D}}\right) \in \left[\mathcal{T}_{\mathrm{S}}^{\mathrm{min}},\mathcal{T}_{\mathrm{S}}^{\mathrm{max}}\right] $； (6) WHILE$ \left|\mathcal{T}_{\mathrm{S}}^{\mathrm{max}}-\mathcal{T}_{\mathrm{S}}^{\mathrm{min}}\right| \lt \varepsilon_1 $DO： (7) 求解感知优化问题，$ \mathcal{T}_{\mathrm{S}}^{\mathrm{min}}=\dfrac{1}{2}\left(\mathcal{T}_{\mathrm{S}}^{\mathrm{min}}+\mathcal{T}_{\mathrm{S}}^{\mathrm{max}}\right) $，否则
$ \mathcal{T}_{\mathrm{S}}^{\mathrm{max}}=\dfrac{1}{2}\left(\mathcal{T}_{\mathrm{S}}^{\mathrm{min}}+\mathcal{T}_{\mathrm{S}}^{\mathrm{max}}\right) $；(8) END (9) 若$ {\mathcal{F}}_{{v}}\left({\boldsymbol{b}}_{\mathcal{D}}\right) \gt {\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right) $，使$ \mathcal{F}_{\mathrm{C}}^{\mathrm{min}}={\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right) $， 其他情
况，设置$ \mathcal{F}_{\mathrm{C}}^{\mathrm{max}}={\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right) $；(10) END 
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表 1 满意度模型的仿真参数
模型 数值 满意度评估模型 $ {\varGamma }_{{u}}=12 $ bit/(s·Hz) $ {{P}}_{{u}}=-35 $ dBm $ \left\{{\lambda }_{{u}}{,c}\right\} $={4,7}
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