Channel state information (CSI) acquisition and feedback overhead grows with the number of antennas, users, and reported subbands. This growth becomes a bottleneck for many antenna and reconfigurable intelligent surface (RIS) systems as arrays and user densities scale. Practical CSI feedback and beam management rely on codebooks, where beams are selected via indices rather than explicitly transmitting radiation patterns. Hardware-aware operation requires an explicit representation of the measured antenna/RIS response, yet high-fidelity measured patterns are high-dimensional and costly to handle. We present SPARK (Sparse Parametric Antenna Representation using Kernels), a training-free compression model that decomposes patterns into a smooth global base and sparse localized lobes. For 3D patterns, SPARK uses low-order spherical harmonics for global directivity and anisotropic Gaussian kernels for localized features. For RIS 1D azimuth cuts, it uses a Fourier-series base with 1D Gaussians. On patterns from the AERPAW testbed and a public RIS dataset, SPARK achieves up to 2.8$\times$ and 10.4$\times$ reductions in reconstruction MSE over baselines, respectively. Simulation shows that amortizing a compact pattern description and reporting sparse path descriptors can produce 12.65% mean uplink goodput gain under a fixed uplink budget. Overall, SPARK turns dense patterns into compact, parametric models for scalable, hardware-aware beam management.

翻译：信道状态信息（CSI）的获取与反馈开销随天线数量、用户数量及上报子带数量的增加而增长。随着天线阵列规模和用户密度的提升，这种增长成为许多天线与可重构智能表面（RIS）系统的瓶颈。实用的CSI反馈与波束管理依赖于码本，其中波束通过索引而非显式传输辐射方向图进行选择。硬件感知操作需要天线/RIS实测响应的显式表征，然而高保真实测方向图具有高维度且处理成本高昂。本文提出SPARK（基于核的稀疏参数化天线表征），这是一种无需训练的压缩模型，可将方向图分解为平滑的全局基函数与稀疏的局部波瓣。对于三维方向图，SPARK采用低阶球谐函数表征全局方向性，并使用各向异性高斯核表征局部特征。对于RIS的一维方位角切面，则采用傅里叶级数基函数与一维高斯核。在来自AERPAW测试平台及公开RIS数据集的方向图上，SPARK相比基线方法分别实现了高达2.8倍与10.4倍的重构均方误差降低。仿真表明，在固定上行链路预算下，通过分摊紧凑方向图描述与上报稀疏路径描述符，可获得12.65%的平均上行链路良好吞吐增益。总体而言，SPARK将密集方向图转化为紧凑的参数化模型，为实现可扩展的硬件感知波束管理提供了基础。