This paper considers optimal joint time, frequency, and power allocation for energy-efficient device-to-device (D2D) communications in an overlay cellular network, where multiple D2D transmitters communicate with their corresponding D2D receivers while utilizing the energy harvested from a cellular base station (BS). Due to channel estimation error and feedback delays, the channel state information (CSI) available at the transmitters is usually imperfect, which can significantly deteriorate the energy efficiency (EE) of the considered system. To mitigate the impact of imperfect CSI, we formulate a non-convex robust optimization problem for the maximization of the EE (in bits/Joule) of the system while ensuring quality of service (QoS) requirements on the minimum data rata and the minimum amount of harvested energy for cellular and D2D users, respectively. We reveal that the optimal energy-efficient D2D transmission should exhaust all the harvested energy of each D2D transmitter. Exploiting this fact, we propose an efficient iterative algorithm for obtaining the optimal solution. To strike a balance between performance and computational complexity, we further propose a low-complexity suboptimal algorithm. Simulation results show that compared with several baseline schemes, both the proposed optimal and suboptimal algorithms can significantly improve the EE of wireless-powered D2D communications under imperfect CSI. Moreover, the proposed suboptimal algorithm is close-to-optimal when the number of cellular users are small or the channel estimation error is large.

To meet the ever-growing demand for a higher communicating rate and better communication quality, more and more small cells are overlaid under the macro base station (MBS) tier, thus forming the heterogeneous networks. Small cells can ease the load pressure of MBS but lack of the guarantee of performance. On the other hand, cooperation draws more and more attention because of the great potential of small cell densification. Some technologies matured in wired network can also be applied to cellular networks, such as software-defined networking (SDN). SDN helps simplify the structure of multi-tier networks. In addition, it is more reasonable for the SDN controller to implement cell coordination. In this paper, we propose a method to offload users from MBSs through small cell cooperation in heterogeneous networks. Association probability is the main indicator of offloading. By using the tools from stochastic geometry, we then obtain the coverage probabilities when users are associated with different types of BSs. All the cell association and cooperation are conducted by the SDN controller. Then, on this basis, we compare the overall coverage probabilities, achievable rate, and energy efficiency with and without cooperation. Numerical results show that small cell cooperation can offload more users from MBS tier. It can also increase the system's coverage performance. As small cells become denser, cooperation can bring more gains to the energy efficiency of the network.

This paper evaluates the performance of downlink information transmission in three-dimensional (3D) unmanned aerial vehicle (UAV) networks, where multi-tier UAVs of different types and flying altitudes employ directional antennas for communication with ground user equipments (UEs). We introduce a novel tractable antenna gain model, which is a nonlinear function of the elevation angle and the directivity factor, for directional antenna-based UAV communication. Since the transmission range of a UAV is limited by its antenna gain and the receiving threshold of the UEs, only UAVs located in a finite region in each tier can successfully communicate with the UEs. The communication connectivity, association probability as well as coverage probability of the considered multi-tier UAV networks are derived for both line-of-sight (LoS) and non-line-of-sight (NLoS) propagation scenarios. Our analytical results unveil that, for UAV networks employing directional antennas, a necessary tradeoff between connectivity and coverage probability exists. Consequently, UAVs flying at low altitudes require a large elevation angle in order to successfully serve the ground UEs. Moreover, by employing directional antennas an optimal directivity factor exists for maximizing the coverage probability of the multi-tier UAV networks. Simulation results validate the analytical derivations and suggest the application of high-gain directional antennas to improve downlink transmission in the multi-tier UAV networks.

This work was supported in part by the China MOST Program of International S&T Cooperation under Grant 2016YFE0133000 and Grant 2016YFE0119000, in part by the National Natural Science Foundation of China (NSFC) under Grant 61461136004, in part by NSFC Major International Joint Research Project under Grant 61210002, in part by the Fundamental Research Funds for the Central Universities under Grant 2015XJGH011, in part by EU Horizon 2020 Program EXCITING project under Grant 723227, in part by EU FP7 CROWN project under Grant PIRSES-GA-2013-610524, and in part by the China International Joint Research Center of Green Communications and Networking under Grant 2015B01008.

This paper evaluates the performance of uplink transmission in an LTE/Wi-Fi coexisting heterogeneous network (HetNet), where user equipments (UEs) capable of jointly performing LTE/Wi-Fi dual access and dual power control have a higher preference in connecting the Wi-Fi access points (WAPs) than the LTE base stations (LBSs). Thereby, each UE competes for Wi-Fi connection using carrier-sense multiple access with collision avoidance (CSMA/CA) and, only if unsuccessful, employs cellular communication. During uplink transmission, the UEs employ truncated channel inversion power control with receiving thresholds adjusted according to the occupied communication modes. Based on the stochastic geometry theory, we derive the analytical expressions for the successful connection probability and the average energy efficiency (EE) of Wi-Fi communications. We show that there exists a maximal successful connection probability for Wi-Fi communications, where the optimal receiving signal and energy thresholds of WAPs depend critically on the density of WAPs. Moreover, the average EE of UEs successfully accessing WAPs saturates in the large receiving signal threshold regime, independent of the density of WAPs. These findings significantly differ from those in cellular communications, where maintaining stable service connection is more emphasized than other operation goals, and reveal that deploying Wi-Fi communication has a potential for improving the EE of the considered HetNet. Both simulation and experiment results validate the analytical derivations. These results together show that, compared with single-mode uplink transmission, the considered dual-mode transmission can achieve higher connection probability and EE simultaneously in LTE/Wi-Fi coexisting HetNets.

The integration of large models and multimodal foundation models into the low-altitude economy is driving a transformative shift, enabling intelligent, autonomous, and efficient operations for low-altitude vehicles (LAVs). This article provides a comprehensive analysis of the role these large models play within the smart integrated lower airspace system (SILAS), focusing on their applications across the four fundamental networks: facility, information, air route, and service. Our analysis yields several key findings, which pave the way for enhancing the application of large models in the low-altitude economy. By leveraging advanced capabilities in perception, reasoning, and interaction, large models are demonstrated to enhance critical functions such as high-precision remote sensing interpretation, robust meteorological forecasting, reliable visual localization, intelligent path planning, and collaborative multi-agent decision-making. Furthermore, we find that the integration of these models with key enabling technologies, including edge computing, sixth-generation (6G) communication networks, and integrated sensing and communication (ISAC), effectively addresses challenges related to real-time processing, resource constraints, and dynamic operational environments. Significant challenges, including sustainable operation under severe resource limitations, data security, network resilience, and system interoperability, are examined alongside potential solutions. Based on our survey, we discuss future research directions, such as the development of specialized low-altitude models, high-efficiency deployment paradigms, advanced multimodal fusion, and the establishment of trustworthy distributed intelligence frameworks. This survey offers a forward-looking perspective on this rapidly evolving field and underscores the pivotal role of large models in unlocking the full potential of the next-generation low-altitude economy.