The emerging mobile edge computing (MEC) evolutionarily extends the cloud services to the network edge. In order to efficiently coordinate distributed edge resources, software defined networking (SDN) at the network edge has been explored to realize the integrated management of communication, computation, and cache (3C) resources. However, many research efforts, in software-defined edge networks, are mainly devoted to 1C or 2C resource sharing. Motivated by high service performance and user demands, we propose a user-centric edge resource sharing model for software-defined ultra-dense network (SD-UDN) where multiple MEC servers around small base stations (SBSs) can share their 3C resources through OpenFlow-enabled switches. In particular, the service models of MEC servers and users are formulated to optimize the service process by minimizing the service delay, which is NP-hard. To address this NP-hard issue, a service association model is constructed based on design structure matrix (DSM), and a simulated annealing algorithm is employed to further optimize the service association model for reducing time complexity and offering a nearoptimal solution. Compared with traditional 1C or 2C resource sharing, the proposed edge resource sharing model can guarantee lower service delay for users.

Recent studies have shown that traffic safety and efficiency can be substantially improved by vehicle platooning, in which the platoon' stability is ensured through the exchange of control information among vehicles. So, it is a key issue to share control information efficiently and timely. Proximity-based device-to-device (D2D) communications with high transmission rate, can provide a promising solution to the above issue. In this paper, a platoon leader evaluation-based two-stage platoon formation algorithm is proposed to form stable platoons, which can significantly reduce the spectrum resource overhead and improve the safety of vehicle platooning. After that, we propose a predecessor-following communication mode-based on D2D, ensuring high-rate transmission of control information. Moreover, we propose time division-based intra-platoon and minimum rate guaranteed inter-platoon resource allocation mechanisms, which aims at allocating resources for D2D users efficiently within the platoon and optimize cellular users' rate, respectively. Numerical results show that the proposed mechanism and algorithm can not only significantly improve spectrum resource utilization, but also guarantee the platoon stability.

The continuing advances in the storage and transmission capabilities of smart device have made it possible to share multimedia services through device-to-device (D2D) communications. However, when D2D users transmit multimedia services in the manner of cooperative communication, the cooperative users with non-cooperative behavior may lead to a sharp decline in the quality of multimedia services. In our paper, a socially aware trust framework for multimedia delivery is proposed to choose the trustworthy D2D cooperative user. By considering the different D2D cooperative scenarios, the trust relationships of entities in the network are divided into two cases: capability trust mainly from Base station (BS) to cooperative user and social trust from sending user to cooperative user. In particular, capability trust is quantified based on the service capability of user, such as caching capability, processing capability and transmission capability, and social trust is obtained based on historical interactive behavior of users, such as cooperative behavior, altruistic behavior and reciprocal behavior. By analyzing the two types of trust, the trustworthy D2D cooperative users are selected for supporting different cooperative scenarios. The numerical results show that the proposed trust framework can effectively identify the selfish users and notably enhance the delivery efficiency of multimedia services, such as delay and delivery success ratio.

In this paper we demonstrate a double-resonant Huygens' metasurface. The double Huygens' resonances serve as the bridges connecting several non-resonant passbands, achieving good transmission performance over an ultrawideband range and available transmission phase coverage far beyond 360°. Then a dual-band dual-polarized 3-bit phase scheme is proposed to independently obtain sufficient phase coverages at 28/38GHz. When used for planar lens antenna design, good dual-band radiation performance is achieved. In the 28GHz band, the measured peak gain is 29.14dBi at 28.5GHz with an aperture efficiency of 44.87% and a 3-dB gain bandwidth of 18.2%. In the 38GHz band, the measured peak gain is 32.03dBi at 38GHz with an aperture efficiency of 47.25% and a 3-dB gain bandwidth of 8.7%.

Unmanned Aerial Vehicles (UAVs), which are a crucial element of the future air-space-ground integrated network, can serve as potential mobile edge computing (MEC) nodes due to their onboard capabilities for storage, communication, and computation. However, current UAV-assisted MEC collaborative offloading methods primarily focus on addressing network requirements for computing tasks, while neglecting the heterogeneity in task demands due to the heterogeneity of task types and UAV service types. To address this, we propose a task demand-oriented collaborative offloading and deployment strategy in software-defined UAV-assisted edge networks. Specifically, to enhance the cache utilization of UAVs, we introduce an SD-UEN (Software-Defined UAV-Edge Network) architecture, which facilitates cooperation among UAVs under the guidance of an SDN (Software-Defined Networking) controller. In light of the heterogeneity of task demands, we employ the Tabu Search-based Matching (TSM) algorithm to accurately match computing tasks with the appropriate UAV modes. Furthermore, to enable intelligent UAV mode switching and dynamic UAV location deployment, we leverage the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By centrally training the MADDPG model offline, MEC servers and UAVs, acting as learning agents, can efficiently adjust UAV modes and deploy UAVs during online execution. This algorithm dynamically optimizes UAV actions to minimize task completion time and energy consumption. The simulation results highlight that our algorithm substantially reduces task response time and energy consumption compared to other algorithms, demonstrating its effectiveness.