Relay-enabled cooperative communication has been a hot topic in the area of the Internet of Things (IoT), for its help in providing important solutions to resource limitations in IoT communication scenarios. In this article, we study the outage minimization problem in a power-limited decode-and-forward (DF) relaying network with environmental uncertainty. To reduce the outage probability and improve the quality of service, existing researches usually rely on the assumption of both exact instantaneous channel state information (CSI) and environmental uncertainty. However, it is difficult to obtain perfect instantaneous CSI immediately under practical situations where channel states change rapidly, and the uncertainty in communication environments may not be observed, which makes traditional methods not applicable. Therefore, we turn to reinforcement learning (RL) methods for solutions, which do not need any prior knowledge of underlying channels or assumptions of environmental uncertainty. The RL method is to learn from the interaction with the communication environment, optimize its action policy, and then propose relay selection and power allocation schemes. In this work, we first analyze the robustness of RL action policy by giving the lower bound of the worst case performance, when RL methods are applied to communication scenarios with environment uncertainty. Then, we propose a robust algorithm for outage probability minimization based on RL. Simulation results reveal that compared to traditional RL methods without robust design, our approach has good generalization ability and can improve the worst case performance by about 4%.

Modern inertial measurement units (IMUs) with low cost, small size, and low power consumption are the key to improving indoor positioning accuracy. However, the performance of IMU in current mobile phones is spotty. The IMU positioning error of mainstream cell phones is basically greater than 1%, which makes it difficult for indoor fusion positioning technology to achieve accuracy within 3 m on a mobile phone, seriously restricting the development of the industry. Therefore, the existing indoor fusion algorithms need to introduce additional hardware, such as high-density UWB/Wi-Fi/Bluetooth deployment, to compensate for the lack of IMU performance, which significantly increases the deployment cost, difficulty, and reduces environmental applicability. It is found that deep learning can greatly improve the performance of IMU, but traditional deep learning methods have problems, such as privacy protection, difficulty in data collection, and low training efficiency. Therefore, we propose a novel data-driven inertial navigation method based on federated learning, named FedINS, to improve IMU performance and solve the above-mentioned problems. In order to further improve performance and reduce hardware cost, we introduce the concept of segment fusion. FedINS2 (2 means the second S, which is the segment) is formed by combining FedINS with low-cost edge-end ranging, which has terminal computing capabilities and edge-end ranging capabilities. FedINS2 not only greatly improves the performance of the smartphone IMU from 3.6% to 0.8%, but also has the characteristics of privacy protection and efficient data collection. Experimental results demonstrate that the proposed data-driven inertial navigation algorithm is effective.

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold.

Not only has information technology evolved rapidly, but the spatial cognition theory for blind and visually impaired (BVI) people has also made great strides, which has opened up a new opportunity for indoor travel assistance systems (ITASs). However, there are still some issues that have not been effectively addressed due to the lack of guidance of the spatial cognition theory. Thus, this article presents a comparative survey among ITASs proposed in the last four years in an effort to inform researchers and developers about system problems and challenges and inform BVI people about the various types and functions of the ITAS. This article will also make researchers and developers aware of the importance of the spatial cognition theory. Furthermore, we give predictions for future trends based on a detailed analysis of 17 ITASs.

The advantages of millimeter-wave and large antenna arrays technologies for accurate wireless localization have received extensive attentions recently. However, how to further improve the accuracy of wireless localization, even in the case with obstructed line-of-sight, is largely undiscovered. In this paper, the reconfigurable intelligent surface (RIS) is introduced into the system to make the positioning more accurate. First, we establish the three-dimensional RIS-assisted wireless localization channel model. After that, we derive the Fisher information matrix and the Cramér-Rao lower bound for evaluating the estimation of absolute mobile station position. Finally, we propose an alternative optimization method and a gradient decent method to optimize the reflect beamforming, which aims to minimize the Cramér-Rao lower bound to obtain a more accurate estimation. Our results show that the proposed methods significantly improve the accuracy of positioning, and decimeter-level or even centimeter-level positioning can be achieved by utilizing the RIS with a large number of reflecting elements.

Reconfigurable intelligent surface (RIS) technology can significantly improve the energy and spectrum efficiency of wireless communication systems. Most existing studies were conducted with an assumption of ideal hardware, while the impact of hardware impairments receives little attention. However, the non-negligible hardware impairments should be taken into consideration when we evaluate the system performance. In this paper, we consider an RIS assisted communication system with hardware impairments, and focus on the channel estimation study and the power scaling law analysis. First, with linear minimum mean square error estimation, we theoretically characterize the relationship between channel estimation performance and impairment level, number of reflecting elements, and pilot power. After that, we analyze the power scaling law and reveal that if the base station (BS) has perfect channel state information, the transmit power of user can be made inversely proportional to the BS antenna number and the square of the reflecting element number with no reduction in performance; If the BS has imperfectly estimated channel state information, to achieve the same performance, the transmit power of user can be made inversely proportional to the square-root of the BS antenna number and the square of the reflecting element number.