Reconfigurable intelligent surfaces (RISs) are considered as an efficient solution for the implementation of extremely large-scale MIMO (XL-MIMO). To mitigate the complexity of channel information acquisition, beam training is identified as an effective solution by selecting the optimal beam from a predefined codebook. However, practical hardware limitations of RIS result in the overlap of codeword coverage, i.e., non-orthogonal beams, which degrades the accuracy of optimal beam selection. In this paper, we propose a self-correcting hierarchical beam training scheme, where beam training is designed as a sequential process based on a tailored long short-term memory network. Received powers of sequentially selected multi-layer codewords are integrated to construct quasi-orthogonality for optimizing the beam selection. Beam training is thus reformulated as a sequential codeword classification problem. A codeword priority adjustment procedure is then designed to prevent the beam selection from looping among layers according to the visit frequency to each codeword of the current selection path. Simulation results show that our proposed method outperforms existing beam training approaches in terms of beam selection accuracy and sum rate in the presence of hardware limitations.

Holographic MIMO communications, enabled by large-scale antenna arrays with quasi-continuous apertures, are potential technology for spectrum efficiency improvement. However, the increased antenna aperture size extends the range of the Fresnel region, leading to a hybrid near-far field communication mode. The users and scatterers randomly lie in near-field and far-field zones, and thus, conventional far-field-only and near-field-only channel estimation methods may not work. To tackle this challenge, we demonstrate the existence of the power diffusion (PD) effect, which leads to a mismatch between the hybrid-field channel and existing channel estimation methods. Specifically, in far-field and near-field transform domains, the power of one channel path may diffuse to other positions, thus generating fake paths. This renders the conventional techniques unable to detect those real paths. We propose a PD-aware orthogonal matching pursuit (PD-OMP) algorithm to eliminate the influence of the PD effect by identifying the PD range, within which the path power diffuses to other positions. PD-OMP fits a general case without prior knowledge of respective numbers of near-field and far-field paths and the user’s location. Simulation results show that PD-OMP can accurately estimate the channel when antenna spacing is below half wavelength and outperform current state-of-the-art hybrid-field channel estimation methods.

To realize holographic communications, a potential technology for spectrum efficiency improvement in the future sixth-generation (6G) network, antenna arrays inlaid with numerous antenna elements will be deployed. However, the increase in antenna aperture size makes some users lie in the Fresnel region, leading to the hybrid near-field and far-field communication mode, where the conventional far-field channel estimation methods no longer work well. To tackle the above challenge, this paper considers channel estimation in a hybrid-field multipath environment, where each user and each scatterer can be in either the far-field or the near-field region. First, a joint angular-polar domain channel transform is designed to capture the hybrid-field channel's near-field and far-field features. We then analyze the power diffusion effect in the hybrid-field channel, which indicates that the power corresponding to one near-field (far-field) path component of the multipath channel may spread to far-field (near-field) paths and causes estimation error. We design a novel power-diffusion-based orthogonal matching pursuit channel estimation algorithm (PD-OMP). It can eliminate the prior knowledge requirement of path numbers in the far field and near field, which is a must in other OMP-based channel estimation algorithms. Simulation results show that PD-OMP outperforms current hybrid-field channel estimation methods.

In this paper, we investigate the close contact detection for COVID-19 patients based on the heterogeneous mobile edge computing (MEC) framework. Collecting the spatial-temporal data of a large number of mobile users, the base stations equipped with MEC servers organize these data via the R-tree structure. The cloud center (CC) aggregates the spatial-temporal data from all MEC servers. Considering the mobility of users as well as various positions of MEC servers, the CC then partitions and assigns the close contact detection tasks to different servers for faster processing. Aiming to minimize the system latency, we propose a Deep Deterministic Policy Gradient-based task and resource allocation scheme, where the computing loads are balanced among different servers. Simulation results show that a minimum system latency is reached while maintaining the load balance among all servers. Up to 37% detection accuracy enhancement is achieved compared with an existing task allocation scheme without load balance.