The rapid development of the Internet of Vehicles (IoV) enables various vehicular applications, such as image-aided navigation and traffic information management. It is important to provide efficient content delivery services for these vehicular applications. Caching popular content at roadside units (RSUs) is a promising way to improve content delivery efficiency. However, due to RSUs with limited cache space, it is very challenging to develop an effective content delivery policy that satisfies the high quality of service (QoS) requirements for vehicular applications. In this paper, we investigate the user-centric content delivery problem with service delay constraints in the IoV, where the objective is to minimize the vehicle’s cost under usage-based pricing. The problem of finding an optimal content delivery policy is modeled as a finite-horizon Markov decision process (MDP). Since the cache state of each RSU, and the wireless channel qualities between the vehicle and RSUs, are usually unknown to the vehicle a priori, the vehicle must learn the optimal delivery policy by interacting with the environment. To solve this problem and optimize the vehicle’s cost, we propose a double deep Q network (DDQN)-based algorithm, which implements dynamic content delivery decisions. Furthermore, the double deep Q network can overcome the large-scale state space and reduce Q value over-estimation. Numerical results show that our policy achieves a near-optimal performance when compared to the optimal policy that knows precisely cache state and wireless channel state. We also compare the effects of different caching strategies and vehicle mobility on the performance of the algorithm.

Unmanned aerial vehicles (UAV) have emerged as prime technologies due to their compatible size and flexible architecture. UAV technology offers services in vast application such as inter-UAV communication, wireless sensors, and the future Internet of Things (IoT) due to its compatible architecture. A UAV’s speed varies while roaming, which may increase the risk of a connection failure. Various routing schemes have provided solutions to address this essential issue for three-dimensional (3D) UAV-based networks. The main category of UAV routing schemes is position-based routing schemes, which choose the best route based on the UAV’s location. However, position-based routing has the drawback that it depends on exact positioning and tracking. An efficient routing scheme can resolve the significant issue associated with UAV mobility in a 3D environment. This paper aims to address the issues of static preloaded location values by presenting a hybrid routing scheme named the Position-Monitor-based Hybrid Routing Protocol (PMHRP), which takes advantage of both geographic and topology-based routing protocols. The PMHRP establishes the shortest possible route based on a UAV’s Global Positioning System (GPS). Moreover, the proposed protocol utilizes the links for data forwarding. Furthermore, a disaster-based UAV scenario is adopted to provide connections to IoT devices. A detailed comparison analysis shows the proposed scheme’s extreme performance and results in up to 65% to 73% better packet delivery ratio (PDR) than batch mark schemes under standard 3D UAV scenarios. Compared to earlier work, the proposed scheme reduces the average delay by up to 68% to 75%. Further proposed routing schemes offer 70% to 72% more throughput than the existing routing schemes, and NRL (%) is 42% to 49% lower than the existing routing schemes. This happens because of the global routing information available at each UAV which is provided by the position head coordinator (PHC) UAV in the proposed work.

We consider the problem of wideband spectrum sensing, where a multi-antenna secondary user (SU) attempts to identify the frequency locations of a number of single-antenna primary users (PUs)' transmissions that spread over a wide frequency band. To overcome the sampling rate bottleneck, we propose a new sub-Nyquist sampling architecture as well as a compressed power spectrum estimation method whose objective is to reconstruct the power spectrum of the transmitted signals from PUs. The proposed sub-Nyquist sampling scheme has a simple structure, and a power spectrum recovery analysis shows that the proposed method can achieve a significant sampling rate reduction, without imposing a sparse constraint on the frequency band under monitoring. Simulation results are presented to show the effectiveness of the proposed method.

In this letter, the Meta distributions (MDs) of the secrecy rate in downlink non-orthogonal multiple access (NOMA) systems with randomly located eavesdroppers are investigated. Due to the complexity of calculation, the MDs for NOMA users are approximated by Beta approximation and the analytical expressions for the first two moments of conditional secrecy connection probability of NOMA users are derived, while perfect/imperfect successive interference cancellations on the near user are considered. Finally, the accuracy of the result is verified by Monte Carlo simulations. The results show that the MD can capture the statistics of the secrecy rate in a given secrecy connection probability.