Virtual reality (VR) demands extensive computation while imposing strict requirements for ultra-low latency, placing a significant burden on wireless communication systems. In recent years, there has been a growing interest in leveraging unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) as a promising technology to provide flexible computing resources at the edge of wireless networks. To meet the computational demands of VR, we propose a collaborative three-layer edge computing framework assisted by multiple UAVs. This framework enables VR rendering tasks to be executed locally on user devices or offloaded to UAVs and base station (BS) for execution. By jointly optimizing the flight trajectories of UAVs and the rendering modes of users, we aim to maximize the average rendering completion rate, defined as the ratio of successfully completed VR rendering tasks within the specified delay constraints, while minimizing the average energy consumption of UAVs. To enhance adaptability, we adopt a multi-agent twin delayed deep deterministic policy gradient (MATD3) approach that provides an efficient strategy for multi-UAV-assisted VR rendering, even in partially observable scenarios. Simulation results validate our proposed approach and demonstrate that the MATD3 algorithm surpasses the classical multi-agent deep deterministic policy gradient (MADDPG) algorithm in terms of convergence speed and the average rendering completion rate.

This paper considered a data rate maximization problem of relay satellite network and aimed to overcome the difficulty of direct transmission between the satellites and ground station. The proposed optimization problem was first formulated as a mixed integer non-convex programming which is difficult to solve. Then, to design an efficient solving method, the proposed optimization problem were approximately decoupled into two subproblems including relay selection problem and power allocation problem. Through a node virtualization method, the relay selection problem was converted to a maximum weighted matching problem. However, the power allocation problem was still a non-convex problem but could be approximated as convex problem by using difference of convex (DC) programming. Moreover, an iterative algorithm was designed to acquire the optimal solution of approximated convex problem based on the Lagrangian dual method. Finally, simulation results are provided to demonstrate the superiority of the algorithm.

Ambient energy harvesting has emerged as a promising technique to improve the energy efficiency and reduce the total greenhouse gas emissions for green wireless communications. Energy management for throughput maximization under random energy arrivals has been studied extensively in energy harvesting relay communication networks with either finite-size data buffer or finite-size energy storage. However, the problem is still open when the energy harvesting relay node is subject to both finite-size data and energy storage. In this paper, we study the transmission policy of joint time scheduling and power allocation under a transmission deadline, which maximizes the end-to-end system throughput in a two-hop relay communication network where the energy harvesting relay node is equipped with finite-size data buffer and battery. In particular, we first formulate the throughput maximization problem as a convex optimization problem under an offline optimization framework, and obtain the optimal offline time scheduling and power allocation by the Karush-Kuhn-Tucker conditions based on the full knowledge of energy arrivals and channel states. Then, we formulate the throughput maximization problem as a stochastic dynamic programming problem under the online optimization framework, and obtain the optimal online time scheduling and power allocation by solving a series of convex optimizations based on the casual knowledge of energy arrivals and channel states. Finally, to reduce the computation complexity, we further propose two suboptimal online transmission policies. Numerical results show the impacts of battery capacity and buffer size on the maximum throughput of the proposed policies, as well as the balance between the spectrum efficiency and the delay sensitivity.

Abstract This paper investigates the topology control problem with the goal of minimizing mutual interferences in wireless ad hoc networks. It is known that interference is considered as a relationship between link and node in previous works. In this paper, we attempt to capture the physical situation of space‐division multiplex more realistically by defining interference as a relationship between any two bidirectional links. We formulate the pair‐wise interference condition between any two bidirectional links, and demonstrate that the interference condition is equivalent by employing the equal‐power allocation strategy and by employing the minimum‐power allocation strategy. Then we further study the typical interference relationship between a link and its surrounding links. To characterize the extent of the interference between a link and its surrounding links, a new metric, the interference coefficient, is given, and its property is explored in detail by means of analysis and simulation. Based on the insight obtained, a centralized algorithm, BIMA, and a distributed algorithm, LIMA, are proposed to control the network interference. Our simulation indicates that BIMA can minimize the network interference while conserving energy and maintaining good spanner property, and LIMA has relatively good interference performance while keeping low node degree, compared with some well‐known algorithms. Besides, both BIMA and LIMA show good robustness to additive noises in terms of interference performance. Copyright © 2010 John Wiley & Sons, Ltd.

It is known that interference is considered as a relationship between link and node in previous works. In this paper, we attempt to capture the physical situation of space-division multiplex more realistically by defining interference as a relationship between any two undirected links. Here a new metric, the average partial interference coefficients, is given. Then we find that the coefficients are almost independent with node density and can be approximated by different linear functions of the length of link respectively. Based on the insight obtained, a new topology control algorithm, the blocked interference minimum algorithm (BIMA), is proposed to control the network interference. Our simulation indicates that the network topologies produced by BIMA show good performance in terms of network interference, energy cost and node degree.

This paper investigates minimizing mutual interferences in wireless ad hoc networks by means of topology control. Prior work defines interference as a relationship between link and node. This paper attempts to capture the physical situation more realistically by defining interference as a relationship between link and link. We formulate the pair-wise interference condition between two links, and show that the interference conditions for the minimum-transmit-power strategy and the equal-transmit-power strategy are equivalent. Based on the pair-wise definition, we further investigate the "typical" interference relationship between a link and all other links in its surrounding. To characterize the extent of the interference between a link and its surrounding links, we define a new metric called the interference coefficient. We investigate the property of interference coefficient in detail by means of analysis and simulation. Based on the insight obtained, we propose a topology control algorithm - minimum interference algorithm (MIA) - to minimize the overall network interference. Simulation results indicate that the network topologies produced by MIA show good performance in terms of network interference and spanner property compared with known algorithms such as LIFE, Gabriel Graph and k-NEIGH.