AI-Based Beam-Level and Cell-Level Mobility Management for High Speed Railway Communications

High-speed railway (HSR) communications are pivotal for ensuring rail safety, operations, maintenance, and delivering passenger information services. The high speed of trains creates rapidly time-varying wireless channels, increases the signaling overhead, and reduces the system throughput, making it difficult to meet the growing and stringent needs of HSR applications. In this article, we explore artificial intelligence (AI)-based beam-level and cell-level mobility management suitable for HSR communications, including the use cases, inputs, outputs, and key performance indicators (KPI)s of AI models. Particularly, in comparison to traditional down-sampling spatial beam measurements, we show that the compressed spatial multi-beam measurements via compressive sensing lead to improved spatial-temporal beam prediction. Moreover, we demonstrate the performance gains of AI-assisted cell handover over traditional mobile handover mechanisms. In addition, we observe that the proposed approaches to reduce the measurement overhead achieve comparable radio link failure performance with the traditional approach that requires all the beam measurements of all cells, while the former methods can save 50% beam measurement overhead.

Suturing Tasks Automation Based on Skills Learned From Demonstrations: A Simulation Study

In this work, we develop an open-source surgical simulation environment that includes a realistic model obtained by MRI-scanning a physical phantom, for the purpose of training and evaluating a Learning from Demonstration (LfD) algorithm for autonomous suturing. The LfD algorithm utilizes Dynamic Movement Primitives (DMP) and Locally Weighted Regression (LWR), but focuses on the needle trajectory, rather than the instruments, to obtain better generality with respect to needle grasps. We conduct a user study to collect multiple suturing demonstrations and perform a comprehensive analysis of the ability of the LfD algorithm to generalize from a demonstration at one location in one phantom to different locations in the same phantom and to a different phantom. Our results indicate good generalization, on the order of 91.5%, when learning from more experienced subjects, indicating the need to integrate skill assessment in the future.

Deep Plug-and-Play Prior for Massive MIMO Systems

Scalability is a major concern in implementing deep learning (DL) based methods in wireless communication systems. Given various communication tasks, applying one DL model for one specific task is costly in both model training and model storage. In this paper, we propose a novel deep plug-and-play prior method for three communication tasks in the downlink of massive multiple-input multiple-output (MIMO) systems, including channel estimation, antenna extrapolation and channel state information (CSI) feedback. The proposed method corresponding to these three communication tasks employs a common DL model, which greatly reduces the overhead of model training and storage. Unlike general multitask learning, the DL model of the proposed method does not require further fine-tuning for specific communication tasks, but is plug-and-play. Extensive experiments are conducted on the DeepMIMO dataset to demonstrate the convergence, performance, and storage overhead of the proposed method for the three communication tasks.

CSI-PPPNet: A One-Sided Deep Learning Framework for Massive MIMO CSI Feedback

To reduce multiuser interference and maximize the spectrum efficiency in frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a one-sided deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a deep learning (DL) based denoisor in place of the proximal operator of the prior in an alternating optimization scheme. This way, a DL model trained once for denoising can be repurposed for CSI recovery tasks with arbitrary linear projections. In addition to the one-for-all property, in comparison to the two-sided autoencoder-based CSI feedback architecture, the one-sided framework relieves the burden of joint model training and model delivery, and could be applied at UEs with limited device memories and computation power. This opens new perspectives in the field of DL-based CSI feedback. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness of the proposed method.