In the last decade, MIMO spatial multiplexing and distributed beamforming play a significant role in improving data throughput through cooperative transmission. It has been widely used in wireless communication, especially in 6G. However, the distributed uplink beamforming is still an open problem in highly dynamic environments. However, the proposed 6G technology represents the further integration of deep learning and wireless communication. In this article, we propose Argute Distributed Uplink Beamforming (ArguteDUB), which uses a feedback algorithm with an offline-trained deep learning model to implement highly dynamic distributed uplink beamforming for the Internet of Vehicles (IoV) in 6G. Specifically, each vehicle enables the base station (BS)/access point (AP) to separate different channel state information (CSI) by inserting orthogonal sequences into the sending data. The BS adopts deep learning to filter the noise and predict the beamforming weight to achieve phase synchronization. Unlike traditional distributed uplink beamforming, ArguteDUB can be adapted to the highly dynamic time-varying channels. The simple network structure ensures the fast response of ArguteDUB. In addition, we make ArguteDUB Orthogonal Frequency Division Multiplexing (OFDM) compatible so that it can be easily deployed in 6G networks. Our evaluation shows that ArguteDUB has an signal-to-noise ratio (SNR) gain of about 5 dB to 5.3 dB over the single vehicle transmission mode.

Sparse mobile crowdsensing, driven by the increasing ubiquity of smartphones, has emerged as a popular method for data collection. This approach can reconstruct the whole sensing map by inferring missing temporal data from their spatial-temporal correlations to sparse samples. Traditional methods face challenges in capturing non-linear correlations in sensors and error accumulation. To address these issues, we propose a dynamic signal map reconstruction model, i.e., Spatial-Temporal Graph Attention (STGA). Specifically, STGA first applies graph embedding to convert sensing points into graph structures, followed by using a message-passing mechanism based on Graph Attention Networks (GAT) to capture complex spatial-temporal dependent features in sensing data. Then, we propose hierarchical attention mechanism, where different nodes are selected and weighted, and the temporal and spatial sequences of the nodes are stratified separately. By collecting a small amount of observation data, the spatial-temporal characteristics of nodes in the current sparse sensing data graph are effectively learned and the signal graph is updated. Attention mechanism quantitatively evaluates and weights the measurement points to further reduce the measurement error. Evaluations conducted on three typical urban sensing datasets demonstrate the effectiveness of the proposed methods in enhancing the inference accuracy of sparse sensing data.

Practical and accurate localization systems are important to mobile targets that enable promising services such as navigation and augmented reality. With the proliferation of WiFi, existing WiFi-based localization systems have leveraged RSSI, fingerprints, landmarks, time of arrival, or angle of arrival to locate targets, while no related work pays attention to mobile targets themselves. For wheel-driven mobile targets, such as vehicles, bikes, and wheeled robots, we design and implement WheelLoc, a novel WiFi-based localization system leveraging the rotation of wheels. The specially designed WheelLoc hardware is cost-effective and self-powered with the composition of three commercial antennas and a solar cell, which is also easy to be installed on wheels. A hybrid WheelLoc algorithm is further proposed to realize accurate localization in diverse environments, whether the wheel of targets is static or mobile, indoor or outdoor, on flat or bumpy ground. The movements of individual antennas are exploited to emulate linear, cycloid, and circular antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Extensive experiments are conducted on bikes in the real world. Performance results demonstrate that WheelLoc does not require any user interaction, yet achieves comparable accuracy with the state-of-the-art localization systems using WiFi.

VANET refers to the mobile self-organizing network in the actual traffic environment. It is an important part of the Intelligent Transportation System (ITS) and has profound research significance. In VANETs, nodes set up networks to implement the routing and transmission of packets in a mutually cooperative manner. However, due to the dynamic changes in network topology, the short duration of communication links, the frequently switched links and the rapid changes in the surrounding complex environment, the wireless channel is subject to the constantly changing external interference, resulting in a more unstable vehicle network environment. Due to the mobility of vehicles, VANETs have many complex features. The impact of these features makes VANET suffer from high packet loss. The analysis results show that the vehicle density affects the packet loss rate and throughput of VANET. The packet loss rate fluctuates with the increase of vehicle density, and the vehicle speed has no obvious effect on the packet loss rate.