Improving the downlink throughput of multi-UAV air-to-ground mmWave systems under rapid motion and channel uncertainty is a critical challenge. In this letter, UAV states are estimated and the resulting position uncertainty is propagated to the channel statistics through closed-form Jacobians. We propose an alternating optimization mechanism, where a surrogate sum-rate metric is used for inner-layer precoder design and a Riemannian trust-region solver is employed for outer-layer BS reconfiguration. Simulation results demonstrate downlink sum-rate improvements over non-optimized and Euclidean optimization baselines in dynamic multi-UAV mmWave deployments.

The energy-efficient transmission schemes are crucial to realize the Energy Harvesting (EH)-based Device-to-Device (D2D) communications. Multicast, one of the D2D modes, can serve as an effective approach to address the unreliable energy supply of EH-D2D communications and can further improve energy efficiency through cooperation among multiple users, but it has been rarely explored. To achieve the robust and energy-efficient performance for EH-D2D Multicast communications (EH-D2MD), we first design two cooperative transmission schemes: multi-cluster head content cooperation and single-cluster head energy cooperation by integrating the features of D2MD mode, efficient energy management method and wireless power transfer technology. To investigate the effectiveness and adaptability of the two cooperative schemes, we formulate a long-term average energy-efficient utility problem, which allocate the cluster heads, cooperative time and transmission power simultaneously and adaptively. We then propose an Online Convex Approximation (OCA) algorithm that combines the Lyapunov and convex approximation methods to address the non-convex Mixed Integer NonLinear Programming (MINLP) property of the modeled problem. With OCA, we can convert the long-term non-convex MINLP problem into a real-time convex MINLP problem, and obtain an optimal solution for this problem. Results reveal that the achieved energy efficiency of two proposed schemes is at least 10 times higher than that of no cooperation method, and improves at least 50% and up to 4 times compared to the single-slot cooperative algorithms.

In the coming 6G area, the throughput burden of cellular networks is generated by the the explosive growth in the number of smart devices and its requirements. The throughput burden can be mitigated effectively by the Device-to-Device Mul-ticast (D2MD) communication technology-based content sharing. With the aim of gaining the benefit of content sharing of D2MD, it is necessary to adopt an appropriate method for cluster formation that considers adaptivity and comprehensive influence. Thus, to address this method, this paper designs a global rate maximization problem by comprehensively taking into account the impact factors of the physical and social layers. Due to the high complexity of the designed problem, an Evolution-Affinity Propagation (EAP) algorithm is proposed to solve it. Finally, the performance of EAP is verified in simulation results and shows that the performance of EAP is improved by 2.5 times compared with spectral clustering.

The fluctuant and unreliable available energy has been the main reason for limiting the development of Energy Harvesting (EH)-based Device-to-Device (D2D) Communication. Multicasting can further improve energy efficiency by content sharing for EH-D2D communication. However, to satisfy the transmission Quality of Service (QoS) of cluster member with the worst channel state quality, the single cluster head in an EH-based D2D Multicast (EH-D2MD) Communication will face the conflict between energy shortage and consumption due to the requirement of energy consumption of multicasting and the fluctuation of available energy of EH. As a result, the QoS of multicasting transmission will deteriorate rapidly. To address the conflict, this paper designs an Adaptive Content Sharing scheme of Multi-cluster Head (ACS-MH) with the aim of improving the throughput of EH-D2MD. To solve the non-convex Mixed Integer Non-Linear Programming (MINLP) property of the throughput maximization problem and give it a union solution, we propose a three-step Convex Approximate lower-bound Algorithm (3-CDA). Simulations show that ACS-MH can effectively improve the throughput and spectrum efficiency of EH-D2MD.

Recently, Energy Harvesting-supported Device-to-Device (EH-D2D) communication receives extensive concerns due to its excellent properties in Energy Efficiency (EE), offloading capability, etc. Researches on EH-D2D mainly focus on resource allocation schemes of available energy. With the popularity of mobile devices and applications, content sharing becomes anytime, anywhere. Multicast is the foundation to realize the content sharing and can reduce transmission consumption because of multi-user service feature. Thus, Mutlicast can further improve EE of EH-D2D in communication mode rather than in resource allocation. However, the heterogeneity and imbalance of available energy, content requests and social ties on the user side will bring great challenges to the deployment of EH-D2D Multicast (EH-D2MD) communication. This paper aims at EE optimization and establishes a two-layer cluster formation model, which solves grouping, cluster head selection and power control jointly. When facing the non-convex of the sum of fractional functions and the product of binary variables, this paper specifically designs a two-step Convex Approximation Algorithm (CDA). CDA skillfully transforms the modeled non-convex Mixed Integer Non-Linear Programming (MINLP) problem into a convex MINLP one, which is easier to solve. Simulation results reveal that CDA can obtain the joint cluster formation results with approximate optimal EE and lower complexity.

Efficient energy management, especially in terms of energy efficiency (EE) optimization, is the cornerstone to realize the energy harvesting (EH)-powered device-to-device (D2D) communication underlaying cellular network. However, too much focus on energy management is likely to negatively impact other quality-of-service, such as transmission delay. Consequently, this paper aims to investigate an EE and delay tradeoff (EDT) optimization scheme in EH-powered D2D communication underlaying cellular network (EH-DCCN). A more practical scenario, one cellular user and multiple EH-powered D2D pairs matching model and limited energy and data storage space, is considered. In view of the characteristic of the available transmission energy, a weighted EE and delay utility problem is modeled to investigate the EDT optimization scheme. Due to the complexity of the modeled problem, this paper proposes a modified distributed Q-learning (QL) algorithm, namely experience-sharing distributed cooperation learning (EDCL) algorithm, to tackle the EDT optimization issue and enhance the convergence speed. Numerical results discuss convergence speed of EDCL performance, proper experience-sharing interval setting, as well as EDT performance. With the proper experience-sharing interval, EDCL can obtain the near-optimal performance as the classical centralized QL mechanism with more rapid convergence rate by sacrificing the appropriate tolerable additional signaling overhead.