This paper investigates the long-term average age of information (AoI)-minimal problem in an unmanned aerial vehicle (UAV)-assisted wireless-powered communication network (WPCN), which consists of a static hybrid access point (HAP), a mobile UAV, and many static sensor nodes (SNs) randomly distributed on multiple islands. The UAV first is fully charged by the HAP, and then flies to each island to charge SNs and receive data from them. Before running out the energy in battery, the UAV flies back to the HAP to offload the received data and be fully charged again. Due to the finite battery capacity of the UAV, it is impossible for the UAV to traverse all the islands to collect all the data from SNs for once flight. We are thus inspired to divide islands into multiple clusters so that the UAV could traverse all the islands in each cluster. The key factors affecting the long-term average AoI contain the hovering duration, the flying duration, and the amount of data from each island reflected by the number of SNs on each island. Therefore, we formulate the long-term average AoI-minimal problem by jointly optimizing the transmit power of SNs, clustering of islands, and UAV's flight trajectory, subject to the battery capacity of the UAV. Since the optimization problem is NP-hard, there are no standard methods to solve it optimally in general. To tackle this problem, we decouple it into two subproblems: the power allocation subproblem for SNs, and the joint clustering of islands and UAV's flight trajectory design subproblem, which is much more perplexed and complicated owing to the tight coupling between them. To solve the first subproblem, we propose a hybrid TDMA and NOMA (HTN) protocol that takes advantage of the two protocols. To solve the second subproblem, we propose a clustering-based dynamic adjustment of the shortest path (C-DASP) algorithm, which is composed of three sub-algorithms, i.e, a proposed merging-aided K-means clustering (MaKMC) algorithm, the particle swarm optimization (PSO) algorithm employed to find the shortest path in each cluster, and a proposed dynamic adjustment (DA) algorithm taking into account the number of SNs on each island. Simulations verify the effectiveness and superiority of the proposed HTN protocol and C-DASP algorithm.

As a vital metric of information timeliness, Age of Information (AoI) is important for real-time applications in Internet of Things (IoT), such as health monitoring. To satisfy these requirements, we study a wireless-powered IoT (WPIoT), where a static hybrid access point (HAP) coordinates wireless energy transfer to mobile IoT nodes, and mobile IoT nodes transmit data to the HAP or static IoT nodes. We minimize the AoI of mobile IoT nodes by optimizing the selection of the HAP or static IoT node for transmission, the channel selection, the duration of data transmission, and the transmit power, and prove the AoI minimization problem as nondeterministic polynomial hard. To tackle it, we propose a deep deterministic policy gradient (DDPG)-based distributed multinode resource allocation (DDMRA) algorithm, which combines the advantages of the distributed algorithms and centralized algorithms, and combines the selection of discrete actions in the deep Q-network algorithm into the DDPG algorithm. In the DDMRA algorithm, mobile IoT nodes save the energy consumption of transmitting state information to the HAP. Numerical results validate the superior performance of the DDMRA algorithm compared with the baseline algorithms.

The energy harvesting D2D-enabled cellular network (EH-DCN) has emerged as a promising approach to address the issues of energy supply and spectrum utilization. Most of existing works mainly focus on the throughput, while the information freshness, which is critical to the time-sensitive applications, has been rarely explored. Considering above facts, we aim to develop an optimal mode selection and resource allocation (MSRA) policy that maximizes the long-term overall throughput of a time-varying dynamic EH-DCN, subject to an age of information (AoI) constraint. As the MSRA policy involves both continuous variables (i.e., bandwidth, power, and time allocations) and discrete variables (i.e., mode selection and channel allocation), the optimization problem is proved to be nonconvex and NP-hard. To solve the nonconvex NP-hard problem, we exploit a deep reinforcement learning (DRL) approach, called MSRA twin delayed deep deterministic policy gradient (MSRA-TD3). The MSRA-TD3 employs a double critic network structure to better fit the reward function, and could effectively mitigate the overestimation of Q-value in deep deterministic policy gradient (DDPG), which is a classical DRL algorithm. It is worth noting that in the design of the MSRA-TD3, we use the throughput of user equipments (UEs) at the previous time slot as a state to bypass the channel state information estimation resulting from the time-varying dynamic environment, and take the weights of throughput and AoI penalty into the reward function to evaluate two performance. Simulations demonstrate that the established MSRA-TD3 algorithm achieves better performance in terms of throughput and AoI than comparison DRL algorithms.

Internet of things (IoT) is a new paradigm that involves the interconnection of thousands of devices and home appliances. Due to the scarcity of the spectrum suitable for wireless electromagnetic transmission, many communication systems and devices are close to each other, or even overlapping in spectrum, thus incurring complicated interference situations. Therefore, interference in IoT is worthy of thorough investigation and should be well addressed. There have been numerous interference management (IM) proposals at the interfering transmitter or the interfered transmitter/receiver separately or cooperatively. Moreover, the existing IM schemes rely mainly on the use of channel state information (CSI). However, in some communication scenarios, the option to adjust the interferer is not available, and, in the case of downlink transmission, it is always difficult or even impossible for the interfered receiver to acquire necessary information for IM. Based on the above observations, we first propose a novel IM technique, called interference steering (IS). By making use of both CSI with respect to and data carried in the interfering signal, IS generates a signal to modify the spatial feature of the original interference, so that the steered interference at the interfered receiver is orthogonal to its intended signal. We then apply IS to a wireless local area network (WLAN) based IoT in which the same frequency band is reused by adjacent basic service sets (BSSs) with overlapping areas. With IS, multiple nearby access points (APs) could simultaneously transmit data on the same channel to their mobile stations (STAs), thus enhancing spectrum reuse. Our in-depth simulation results show that IS significantly improves network SE over existing IM schemes.

This paper focuses on the QoS-aware secure routing design based on the physical layer security technology for a multi-hop wireless network consisting of legitimate nodes, malicious eavesdroppers, and selfish jammers. We first provide theoretical modeling for a given route to reveal how its end-to-end security/QoS performance is related to the transmitting power of legitimate nodes along the route and the jamming power of jammers in the network. We then design an incentive mechanism that stimulates jammers to generate artificial jamming for security enhancement, and also develop a non-cooperative game framework to resolve the jamming power setting issue here. Based on the security/QoS performance modeling of the route and jamming power setting, we further propose a theoretical framework to determine the optimal transmitting power of nodes along the route such that its optimal transmission security can be achieved under a QoS constraint. Finally, with the help of the power setting results of the given route, we formulate a shortest weighted path-finding problem to identify the optimal route for data delivery in the network, which can be solved by employing the Bellman-Ford or Dijkstra's algorithm. It is demonstrated that the proposed routing scheme is individually rational, stable, distributed and computationally efficient.

This paper focuses on a full-duplex multiple-input multiple-output two-way untrusted relay system, and investigates the optimal beamforming design of such system to maximize its secrecy sum rate (SSR) based on the physical layer security technology. We first provide the modeling of SSR under a general beamforming setting as well as the theoretical formulation of the optimal beamforming design problem. Based on the ideal assumption that the full channel state information is available, we then develop a novel theoretical framework to solve the optimal design problem and thus establish an upper bound on SSR, where the techniques of alternate optimization, fractional programming, semi-definite programming and barrier function method are jointly employed. With the consideration of the constraints in practical implementations, we further propose two sub-optimal beamforming design solutions based on either Wiener prediction or asymptotic approximation. The issue of how to reduce the computational complexity in the optimal relay beamforming design is also discussed in this paper. Finally, we present extensive numerical results to illustrate our theoretical findings and to demonstrate the performance of the proposed sub-optimal beamforming design solutions.