Extremely large-scale multiple-input multiple-output (XL-MIMO) and terahertz (THz) communications are pivotal candidate technologies for supporting the development of 6G mobile networks. However, these techniques invalidate the common assumptions of far-field plane waves and introduce many new properties. To accurately understand the performance of these new techniques, spherical wave modeling of near-field communications needs to be applied for future research. Hence, the investigation of near-field communication holds significant importance for the advancement of 6G, which brings many new and open research challenges in contrast to conventional far-field communication. In this paper, we first formulate a general model of the near-field channel and discuss the influence of spatial nonstationary properties on the near-field channel modeling. Subsequently, we discuss the challenges encountered in the near field in terms of beam training, localization, and transmission scheme design, respectively. Finally, we point out some promising research directions for near-field communications.
Light detection and ranging (LiDAR) sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving, robotics, and virtual reality (VR). However, the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction. Overcoming limitations is critical for 3D point cloud processing. 3D point cloud object detection is a very challenging and crucial task, in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance. In this overview of outstanding work in object detection from the 3D point cloud, we specifically focus on summarizing methods employed in 3D point cloud processing. We introduce the way point clouds are processed in classical 3D object detection algorithms, and their improvements to solve the problems existing in point cloud processing. Different voxelization methods and point cloud sampling strategies will influence the extracted features, thereby impacting the final detection performance.
Traditional named entity recognition methods need professional domain knowledge and a large amount of human participation to extract features, as well as the Chinese named entity recognition method based on a neural network model, which brings the problem that vector representation is too singular in the process of character vector representation. To solve the above problem, we propose a Chinese named entity recognition method based on the BERT-BiLSTM-ATT-CRF model. Firstly, we use the bidirectional encoder representations from transformers (BERT) pre-training language model to obtain the semantic vector of the word according to the context information of the word; Secondly, the word vectors trained by BERT are input into the bidirectional long-term and short-term memory network embedded with attention mechanism (BiLSTM-ATT) to capture the most important semantic information in the sentence; Finally, the conditional random field (CRF) is used to learn the dependence between adjacent tags to obtain the global optimal sentence level tag sequence. The experimental results show that the proposed model achieves state-of-the-art performance on both Microsoft Research Asia (MSRA) corpus and people’s daily corpus, with F1 values of 94.77% and 95.97% respectively.
This paper investigates the problem of bi-directional secure information exchange for a multiple-input single-output (MISO) broadcast channel in presence of potential and external eavesdroppers capable of decoding the confidential messages. Specifically, a multi-antenna base station (BS) simultaneously sends wireless information and power to a set of dual-antenna mobile stations (MSs) using power splitters (PSs) in the downlink and receives information in the uplink in full-duplex (FD) mode. We address the joint design of the receiver PS ratio and the transmit power at the MSs, the artificial noise covariance, and the beamforming matrix at the BS in order to guarantee the individual secrecy rate and energy harvesting constraints at each receiver, and the signal-to-interference plus noise ratio (SINR) at the BS and MSs. Using semidefinite relaxation (SDR) technique, we obtain solution to the problem with imperfect channel state information (CSI) of the self-interfering channels. Simulation results are presented to demonstrate the performance of our proposed scheme.
