This paper explores the design of beamforming codebooks for the base station (BS) and for the reconfigurable intelligent surfaces (RISs) in an active sensing scheme for uplink localization, in which the mobile user transmits a sequence of pilots to the BS through reflection at the RISs, and the BS and the RISs are adaptively configured by carefully choosing BS beamforming codeword and RIS codewords from their respective codebooks in a sequential manner to progressively focus onto the user. Most existing codebook designs for RIS are not tailored for active sensing, by which we mean the choice of the next codeword should depend on the measurements made so far, and the sequence of codewords should dynamically focus reflection toward the user. Moreover, most existing codeword selection methods rely on exhaustive search in beam training to identify the codeword with the highest signal-to-noise ratio (SNR), thus incurring substantial pilot overhead as the size of the codebook scales. This paper proposes a learning-based approach for codebook construction and for codeword selection for active sensing. The proposed learning approach aims to locate a target in the service area by recursively selecting a sequence of BS beamforming codewords and RIS codewords from the respective codebooks as more measurements become available without exhaustive beam training. The codebook design and the codeword selection fuse key ideas from the vector quantized variational autoencoder (VQ-VAE) and the long short-term memory (LSTM) network to learn respectively the discrete function space of the codebook and the temporal dependencies between measurements.

翻译：本文研究上行链路定位主动感知方案中基站与可重构智能表面的波束赋形码本设计。在该方案中，移动用户通过RIS反射向基站发送导频序列，基站和RIS通过从各自码本中顺序选择波束赋形码字与RIS码字进行自适应配置，以逐步聚焦于用户。现有大多数RIS码本设计并非针对主动感知任务定制——所谓主动感知，即后续码字的选择应依赖于已获得的测量结果，且码字序列需动态地将反射波束聚焦至用户方向。此外，现有多数码字选择方法依赖波束训练中的穷举搜索来识别最高信噪比码字，导致码本规模扩大时产生大量导频开销。本文提出一种基于学习的主动感知码本构建与码字选择方法。该学习方法旨在通过递归地从对应码本中选择基站波束赋形码字与RIS码字序列，在无需穷举波束训练的情况下，随着测量数据的积累逐步定位服务区域内的目标。码本设计与码字选择融合了矢量量化变分自编码器与长短期记忆网络的核心思想，分别用于学习码本的离散函数空间及测量数据间的时序依赖关系。