论文标题
在快速变化的通道中反映表面辅助的MIMO传输的智能两次优化
Two-Timescale Optimization for Intelligent Reflecting Surface-Assisted MIMO Transmission in Fast-Changing Channels
论文作者
论文摘要
智能反射表面（IRS）的应用取决于渠道状态信息（CSI）的知识，并受到频道训练，估计和反馈在快速变化的渠道中的沉重开销的阻碍。本文提出了一种新的两次计算方法，以最大化IRS辅助MIMO系统的平均可实现率（AAR），在该方法中，IRS的配置相对基于统计CSI（S-CSI）的配置相对频繁地基于统计学上的PROPODER和POWER分配，并且经常基于快速基于过时的Intertantantantantanteci（I-CSI）（I-CSI）（I-CSI）。关键的想法是，我们首先揭示了基于过时的I-CSI的最佳小型计算分配产生水结构。鉴于最佳功率分配，开发了新的微型批次采样（MBS）基于粒子群优化（PSO）算法，以优化使用降低的通道样本的大型IRS IRS配置。另一个重要方面是，我们开发了一种模型驱动的PSO算法来优化IRS配置，该配置仅通过使用S-CSI来最大化AAR的下限，并消除了通道样本的需求。模型PSO是MBS-PSO的可靠下限。在AAR和效率方面，模拟证明了新的两次尺度波束形成策略的优越性，并证明了IRS的好处。
The application of intelligent reflecting surface (IRS) depends on the knowledge of channel state information (CSI), and has been hindered by the heavy overhead of channel training, estimation, and feedback in fast-changing channels. This paper presents a new two-timescale beamforming approach to maximizing the average achievable rate (AAR) of IRS-assisted MIMO systems, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the base station precoder and power allocation are updated frequently based on quickly outdated instantaneous CSI (I-CSI). The key idea is that we first reveal the optimal small-timescale power allocation based on outdated I-CSI yields a water-filling structure. Given the optimal power allocation, a new mini-batch sampling (mbs)- based particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS configuration with reduced channel samples. Another important aspect is that we develop a model-driven PSO algorithm to optimize the IRS configuration, which maximizes a lower bound of the AAR by only using the S-CSI and eliminates the need of channel samples. The modeldriven PSO serves as a dependable lower bound for the mbs-PSO. Simulations corroborate the superiority of the new two-timescale beamforming strategy to its alternatives in terms of the AAR and efficiency, with the benefits of the IRS demonstrated.
