Flexible coupler is a promising solution for enhancing wireless network capacity by moving passive couplers around a fixed-position active antenna to reshape the induced currents on passive elements. Motivated by this, this paper proposes a novel flexible coupler array that incorporates additional degrees of freedom (DoF) in radiation pattern reconfiguration and enhanced communication coverage with low hardware cost. Specifically, a new form of mechanical beamforming can be obtained by moving only the passive coupling elements while keeping the active antenna stationary. In addition, the flexible coupler antenna can slide along a rail toward users, thereby enhancing communication coverage. To fully exploit the potential of the flexible coupler array, we formulate a two-timescale sum-rate maximization problem with statistical channel state information (CSI). The antenna position is optimized based on scattering cluster-core statistics in the slow timescale, while mechanical beamforming is optimized based on multipath channel statistics in the fast timescale, subject to movement and energy constraints. To address the coupling between timescales and the high cost of extensive channel sampling, we develop a digital agent framework that leverages an electromagnetic (EM) map to generate statistical channel information for different user and antenna positions. Then, a deep neural network is trained to learn a slow-fast performance (SFP) surrogate. Mechanical beamforming at the fast timescale is obtained by selecting per-antenna radiation patterns from a predefined dictionary via a convex relaxation. Simulation results verify the performance gains achieved by the proposed flexible coupler array and the digital-agent-assisted algorithm.

翻译：柔性耦合器是一种通过移动无源耦合器围绕固定位置的有源天线来重塑无源元件感应电流，从而提升无线网络容量的有前景的解决方案。受此启发，本文提出一种新颖的柔性耦合器阵列，该阵列在辐射方向图可重构方面引入了额外的自由度，并以较低的硬件成本增强了通信覆盖范围。具体而言，仅通过移动无源耦合元件而保持有源天线静止，即可实现一种新形式的机械波束赋形。此外，柔性耦合器天线可沿导轨向用户方向滑动，从而增强通信覆盖。为充分挖掘柔性耦合器阵列的潜力，我们基于统计信道状态信息构建了一个双时间尺度的和速率最大化问题。天线位置在慢时间尺度上根据散射簇核心统计进行优化，而机械波束赋形则在快时间尺度上基于多径信道统计进行优化，并受限于移动和能量约束。为解决时间尺度间的耦合以及大量信道采样成本高的问题，我们开发了一个数字代理框架，该框架利用电磁地图为不同用户和天线位置生成统计信道信息。随后，训练一个深度神经网络来学习慢-快性能代理模型。快时间尺度的机械波束赋形通过凸松弛方法从预定义的码本中选择每个天线的辐射方向图来实现。仿真结果验证了所提出的柔性耦合器阵列及数字代理辅助算法的性能增益。