Cell-Free Joint Beamforming and AP Matching Optimization for Integrated sensing and Communication
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摘要: 通信感知一体化(Integrated Sensing and Communication，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(Access Point，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，提出一种基于二进制无线电地图(Binary Radio Map, 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。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 /
- AP-用户/目标匹配 /
- 二进制无线电地图
Abstract:Objective With the ongoing research into sixth-generation mobile communication technology (6G), Integrated Sensing and Communication (ISAC) has been identified as a cornerstone technology for 6G networks. Cell-free is a potential regional coverage architecture for 6G, and the collaboration among access points (APs) resolves issues such as coverage imbalance, interference, and capacity demands in the cellular system. Providing communication and sensing services for low-altitude airspace targets and achieving wide-area continuous coverage based on the cell-free architecture is a feasible solution. However, the research on the cell-free architecture often relies on statistical channel models, which cannot accurately reflect the actual propagation characteristics in complex scenarios. The global channel state information required for optimizing transmission parameters is difficult to obtain, and due to the high dynamics of low-altitude targets, the instantaneous channel state information (CSI) cannot be guaranteed. This paper studies a joint beamforming and AP matching optimization method for ISAC based on BRM. The environmental information provided by BRM is utilized to predict the channels between APs and communication users /sensing targets, providing the global channel information needed for joint optimization. On this basis, an optimization model based on ISAC satisfaction is established, and then an iterative optimization algorithm for AP beamforming and AP-users/targets matching is designed using the genetic algorithm, providing a new approach for AP-users/targets matching. Methods Firstly, the channel between the AP and users/targets is predicted by using the environmental information provided by BRM, and an optimization model based on ISAC satisfaction is established. An iterative optimization algorithm for ISAC beamforming and AP-users/targets matching is designed with the aid of genetic algorithm. However, the communication and sensing are mutually coupled, and the optimization problem has non-convex characteristics and is difficult to solve. Therefore, the optimization problem is further decomposed into two sub-problems of solving the communication and sensing beam vectors. Specifically, in each round of iteration, the beamforming problem is first summarize as a second-order cone programming (SOCP) problem, and an iterative solution algorithm for communication and sensing beams is proposed to effectively solve the communication and sensing beamforming matrix that maximizes the satisfaction function. Then, based on the optimized satisfaction function, an AP matching optimization method based on genetic algorithm is designed. Results and Discussions The simulation results verify the effectiveness of the prediction channel-assisted matching optimization based on BRM. After real channel transmission, compared with the traditional AP matching method based on the shortest path, the transmission power is reduced by approximately 5 dBm, while providing higher users/targets satisfaction( Fig. 7 ). Specifically, as the transmission power increases, the satisfaction of users/targets gradually improves until it reaches 1. However, in the traditional scheme, there is a significant difference between the maximum and minimum satisfaction values when the transmission power remains unchanged(Fig. 8 ). When the transmission power is 40 dBm, the proposed method effectively solves this problem and effectively balances the performance differences among various users/targets. Although the null-space projection scheme leads to a degradation in sensing performance, the minimum received power of the sensing signal remains largely stable. This indicates that the system satisfaction is unaffected, meaning the sensing system can achieve the optimal received power(Fig. 9 ).Conclusions This paper addresses the AP-users/targets matching problem in low-altitude airspace. It utilizes BRM to achieve the matching optimization between AP and low-altitude users/targets, and simultaneously provides communication and perception services for targets in low-altitude airspace. The environmental information provided by BRM is used to predict the channels between AP and users/targets, offering the global channel information required for joint optimization. By maximizing the minimum users/targets satisfaction, the ISAC beam is optimized, and then the iterative optimization of AP- users/targets matching is carried out with the aid of genetic algorithm. Simulation results show that the proposed method can effectively match AP with users/targets, and compared with existing schemes, it can effectively improve the integrated communication and perception performance of the system. -
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}_{\text{m}}^{m\mathrm{ax}} $，CU满意
度范围$ {\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right)\in \left[\mathcal{F}_{{C}}^{\mathrm{min}},\mathcal{F}_{{C}}^{\mathrm{max}}\right] $，ST满意度范围$ {\mathcal{F}}_{{v}}\left({\boldsymbol{b}}_{\mathcal{D}}\right)\in $
$ \left[\mathcal{F}_{{S}}^{\mathrm{min}},\mathcal{F}_{{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}_{{C}}^{\mathrm{min}}+\mathcal{F}_{{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,C}} $；(4) 若$ {{P}}_{{m,C}}\geq {P}_{\text{m}}^{m\mathrm{ax}} $，令$ \mathcal{F}_{{C}}^{\mathrm{max}}={\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right) $并执行步骤(5)；否
则，令$ {{P}}_{{m,S}}={P}_{\text{m}}^{m\mathrm{ax}}-{{P}}_{{m,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}_{{S}}^{\mathrm{min}},\mathcal{T}_{{S}}^{\mathrm{max}}\right] $； (6) WHILE$ \left| \mathcal{T}_{{S}}^{\mathrm{max}}-\mathcal{T}_{{S}}^{\mathrm{min}}\right| \lt {\varepsilon }_{1} $DO： (7) 求解感知优化问题，$ \mathcal{T}_{{S}}^{\mathrm{min}}=\dfrac{1}{2}\left(\mathcal{T}_{{S}}^{\mathrm{min}}+\mathcal{T}_{{S}}^{\mathrm{max}}\right) $，否则
$ \mathcal{T}_{{S}}^{\mathrm{max}}=\dfrac{1}{2}\left(\mathcal{T}_{{S}}^{\mathrm{min}}+\mathcal{T}_{{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}_{{C}}^{\mathrm{min}}={\mathcal{F}}_{{u}}\left({\boldsymbol{b}}_{\mathcal{D}}\right) $， 其他情
况，设置$ \mathcal{F}_{{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 $ {\Gamma }_{{u}}=12 $ bit/s/Hz $ {{P}}_{{u}}=-35 $ dBm $ \left\{{\lambda }_{{u}}{,c}\right\} $={4,7}
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