高速增长的网络数据流量和海量的用户设备等需求要求第五代移动通信（The 5th Generation Mobile Communication，5G）提供更高容量的通信环境。虽然毫米波（Millimeter Wave，mmWave）通信和超密集网络（Ultra-Dense Network，UDN）等技术被提出解决以上问题，但这些技术在多基站场景中实现大规模覆盖时面临信号传播易被遮挡和硬件成本过高等问题。因此在未来第六代移动通信网络（The 6th Generation Mobile Communication ，6G ）中，部署多个低成本的智能超表面（Reconfigurable Intelligent Surface，RIS）将成为有效的解决方案。而在实际多基站多用户场景中合理设计多RIS的部署位置，不仅可以进一步降低硬件支出和减少覆盖盲区，还能够为用户提供更高效的通信服务。因此，本文针对实际多基站多用户场景中信号传播易被遮挡的问题，对多RIS的部署位置进行优化设计，以提高用户平均接收功率与系统容量。

       首先，针对实际通信网络中信号传播易受障碍物遮挡的问题，本文在多基站多用户且有障碍物的通信系统中基于自由空间传播模型以最大化用户平均接收功率为目标提出了一种多RIS位置部署优化方法。该系统中多用户基于最小路径损耗关联基站或RIS，在此基础上建立以多用户平均接收功率为目标的优化模型。由于部署优化问题解空间较大，提出基于遗传算法（Genetic Algorithm，GA）设计多RIS部署方 案，并利用最佳个体的马尔可夫链分析算法的收敛性。数值仿真结果表明在每个RIS的单元数为200时所提RIS部署方案在所考虑的发射功率范围内与无RIS辅助的通信方案相比，用户平均接收功率提高了53.97%。

       其次，在多RIS辅助多基站多用户且有障碍物的通信系统中，由于时变信道影响多RIS部署的稳定性，本文基于统计信道状态信息，设计多RIS位置部署与被动波束赋形，以最大化系统遍历容量。该系统中多用户基于最小路径损耗与基站或RIS建立通信关联，在此基础上基于关联策略建立信道模型，并将系统遍历容量作为优化指标设计多RIS位置部署与被动波束赋形。由于遍历容量含有期望，确切表达式难以获得，因此推导出遍历容量易于处理的上界，再以交替优化的方式设计被动波束赋形与多RIS位置部署。数值仿真结果显示所提RIS部署方案在所考虑的反射单元数目范围内的系统遍历容量分别是 RIS部署在基站侧方案、部署在用户侧方案、BS-RIS-UE固定关联方案、RIS基于障碍物随机部署方案和利用齐次泊松点过程（Homogeneous Poisson Point Processes，HPPP）建模RIS位置方案的107.77%、114.25%、123.92%、133.35%和 142.63%。这表明在资源受限的情况下，可以通过优化RIS部署位置来提高通信系统性能。

     The 5th Generation Mobile Communication (5G) is required to provide a higher capacity communication environment due to the high growth of network data traffic and the huge number of user devices. While technologies such as Millimeter Wave (mmWave) communications and Ultra-Dense Network (UDN) have been proposed to address these issues, they face problems of signal propagation blockage and high hardware costs when achieving large scale coverage in multi-BS scenarios. Therefore, the deployment of multiple low-cost Reconfigurable Intelligent Surfaces (RIS) will be an effective solution in the future 6th Generation Mobile Communication (6G) network. The deployment of multi-RIS in a multi-BS multi-UE scenario can not only further reduce hardware expenditure and coverage blind spots, but also provide more efficient communication services to users. Therefore, this paper addresses the problem that signal propagation is easily blocked in actual multi-BS multi-UE scenario, and optimizes the design of the deployment positions of multi-RIS to improve the average user received power and system capacity.

     First, to address the problem that signal propagation is easily blocked by obstacles in real communication networks, this paper proposes a multi-RIS positions deployment optimization method based on a free-space propagation model with the objective of maximizing the average received power of users in a multi-BS multi-UE communication system with obstacles. The multi-UE in this system is based on the minimum path loss associated BS or RIS, on the basis of which an optimization model is developed with the objective of the average received power of the multi-UE. Due to the large solution space of the deployment optimization problem, the Genetic Algorithm (GA) based multi-RIS deployment scheme is proposed and the convergence of the algorithm is analyzed using Markov chains of the best individuals. The numerical simulation results show that when the number of units in each RIS is 200, the proposed RIS deployment scheme improves the average user received power by 53.97% compared to the communication scheme without RIS assistance within the considered transmission power range.

      Secondly, in a multi-RIS-assisted multi-BS multi UE  communication system with obstacles, as the time-varying channel affects the stability of multi-RIS deployment, this paper designs multi-RIS positions deployment with passive beamforming based on statistical channel state information to maximize the system ergodic capacity. In this system, multi-UE establish communication association with the BS or RIS based on minimum path loss, based on this association strategy to establish a channel model, and use the system ergodic capacity as an optimization index to design multi-RIS positions deployment and passive beamforming. Since the ergodic capacity contains expectations and exact expressions are difficult to obtain, an upper bound on the ergodic capacity is derived that is easy to handle, and then the passive beamforming and multi-RIS positions deployment are designed in an alternating optimization manner. The numerical simulation results show that the system ergodic capacity of the proposed RIS deployment scheme within the range of the number of reflection units considered is 107.77%, 114.25%, 123.92%, 133.35% and 142.63% of the RIS position scheme of the RIS deployment scheme deployed on the side of the BS, the scheme deployed on the UE side, the BS-RIS-UE fixed association scheme, the RIS random deployment scheme based on obstacles, and the scheme using homogeneous Poisson Point Processes (HPPP) to model the RIS position. This indicates that in resource constrained situations, communication system performance can be improved by optimizing the deployment position of RIS.

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