In this paper, the buffer-constrained throughput performance of a multi-user wireless-powered communication (WPC) system is investigated, where energy harvesting follows a non-linear model. The investigation focuses on the buffer overflow performance of sending data in the downlink (DL) from the access point (AP) node to each user equipment (UE) node and that in the uplink from each UE node to the AP node, based on which the throughput performance on both directions when a buffer constraint is enforced is studied. Specifically, the buffer overflow probability at each node is analyzed, based on which the buffer-constrained throughput is studied. In addition, to ensure the throughput performance under the buffer constraint, the DL transmission power allocation policy and the required energy storage capacity at each UE are investigated. Also, the optimal channel time allocation policy is studied with the objective of maximizing the minimum buffer-constrained throughput guaranteed to each UE at the same time. To this aim, an optimization problem is first formulated and then a dichotomy-based time allocation algorithm combined with a one-dimensional search is proposed to solve this problem. The analysis and results, explicitly relating the throughput to the buffer constraint in addition to WPC characteristics, shed new light on the design and performance analysis of WPC systems.

With the growing popularity of wearable devices, body-centric networks have become the focus of Internet of Things (IoT) research. However, malicious eavesdroppers and attackers pose threats to user privacy and network secrecy. Fortunately, physical layer security (PLS) communication and covert communication (CC) have been introduced and can be used in body-centric networks. However, network performance of both secrecy techniques has not been analyzed with an accurate wireless fading model. To fill this research gap, we study a body-centric IoT communication system with information secrecy features based on PLS and CC techniques. By adopting the Alternate Rician Shadowed fading model, we derive closed-form expressions for the secrecy outage probability and secrecy rate in PLS, and the detection error probability and covert rate in CC. To provide more explicit guidance, an algorithm for the selection of PLS and CC techniques is proposed, which aims to reach a high transmission performance state while ensuring communication security and covertness. The secrecy rate and covert rate are analytically and empirically compared. We insightfully find that, with the help of a friendly jammer, CC performs better than PLS, especially with high transmit power. However, an upper boundary of transmit power exists to ensure communication covertness in CC.

The growing antenna array scale, the uncorrelated fadings between downlink and uplink of frequency division duplex (FDD) or analog beamforming design increases the difficulty of channel sounding or estimation. Non-wireless channel detection or beam weight prediction method is a promising solution to help obtain timely and accurate wireless channel state. Beamforming can be enhanced by the powerful sensing capability of cameras, for which this paper proposes a straightforward beam weight prediction method by implementing convolutional neural network (CNN) on images from cameras and a loss function based on chordal distance for this paper’s task. Then a fusion method of visual detection and wireless sounding is developed to further improve spectral efficiency. This fusion method also utilizes a codeword rotation mechanism with Householder transform to save the notification overhead of visual detection results. A testbed has been built to verify the proposed approach with field measurement data. The proposed straightforward method is able to reach high spectral efficiency performance, and the fusion method could outperform exclusive visual detection or wireless sounding with appropriate hierarchical codebook.

The Ultra-Reliable Low-Latency Communication (URLLC) is expected to be an important feature of 5G and beyond networks. Supporting URLLC in a resource-efficient manner demands optimal Modulation and Coding Scheme (MCS) selection and spectrum allocation. This paper presents a study on MCS selection and spectrum allocation to support URLLC. The essential idea is to establish an analytical connection between the delay and reliability requirements of URLLC data transmission and the underlying MCS selection and spectrum allocation. In particular, the connection factors in fundamental aspects of wireless data communication include channel quality, coding and modulation, spectrum allocation and data traffic characteristics. With this connection, MCS selection and spectrum allocation can be efficiently performed based on the delay and reliability requirements of URLLC. Theoretical results in the scenario of a 5G New Radio system are presented, where the Signal-to-Noise Ratio (SNR) thresholds for adaptive MCS selection, data-transmission rate and delay, as well as spectrum allocation under different configurations, including data duplication, are discussed. Simulation results are also obtained and compared with the theoretical results, which validate the analysis and its efficiency.

The 3D MIMO in 5G system requires adaptive and real-time adjustment of antenna azimuth angle, downtilt and beam combination according to the user distribution, which is a powerful technique to improve coverage and capacity. However, due to the complicated interactions between cells and complex environments, it is challengeable to jointly tune the antenna of multiple cells. Besides, the user distribution is complicated because of huge number of users and variable user locations. In this paper, a practical real-time 3D MIMO antenna tuning method with deep reinforcement learning (DRL) is proposed to jointly adjust the antenna configuration according to time-varying user distributions to improve the coverage and access performance of the system. Specifically, a deep neural network is trained to configure the antenna parameters and the Multi-agent reinforcement learning (MARL) is used to adapt various user distributions. The proposed method has been verified on a 5G simulation environment with real geographical features from the real network of China Mobile. Simulation results indicate that the proposed method can improve the coverage and access performance compared with the typical schemes used in practical networks.