Because of the advantages of computation complex- ity compared with
traditional localization algorithms, fingerprint based localization is getting
increasing demand. Expanding the fingerprint database from the frequency domain
by channel reconstruction can improve localization accuracy. However, in a
mobility environment, the channel reconstruction accuracy is limited by the
time-varying parameters. In this paper, we proposed a system to extract the
time-varying parameters based on space-alternating generalized expectation
maximization (SAGE) algorithm, then used variational auto-encoder (VAE) to
reconstruct the channel state information on another channel. The proposed
scheme is tested on the data generated by the deep- MIMO channel model.
Mathematical analysis for the viability of our system is also shown in this
paper.

基于指纹定位的通道重建，利用空间交替广义期望极大化算法提取时变参数，并利用变分自动编码器在另一个通道上重建通道状态信息。在深度多输入多输出通道模型产生的数据上测试了该方案，并在本文中展示了我们系统的可行性的数学分析。

多波段指纹定位的基于新型跨频段 CSI 预测方案

A Novel Cross-band CSI Prediction Scheme for Multi-band Fingerprint  based Localization

Building upon recent works on linesearch-free adaptive proximal gradient
methods, this paper proposes AdaPG$^{\pi,r}$, a framework that unifies and
extends existing results by providing larger stepsize policies and improved
lower bounds. Different choices of the parameters $\pi$ and $r$ are discussed
and the efficacy of the resulting methods is demonstrated through numerical
simulations. In an attempt to better understand the underlying theory, its
convergence is established in a more general setting that allows for
time-varying parameters. Finally, an adaptive alternating minimization
algorithm is presented by exploring the dual setting. This algorithm not only
incorporates additional adaptivity, but also expands its applicability beyond
standard strongly convex settings.

基于最近的无线搜索自适应近端梯度方法，本文提出了 AdaPG^rπ 框架，通过提供更大的步长策略和改进的下界，统一和扩展了现有的结果。讨论了参数 π 和 r 的不同选择，并通过数值模拟证明了所得方法的有效性。为了更好地理解基本理论，还建立了一个允许时变参数的更一般的收敛性设置。最后，通过探索对偶设置，提出了一种自适应交替最小化算法。这种算法不仅包含了额外的适应性，而且扩展了其适用性，超出了标准强凸设置。