Time allocation optimization and trajectory design in UAV-assisted energy and spectrum harvesting network

The scarcity of energy resources and spectrum resources has become an urgent problem with the exponential increase of communication devices. Meanwhile, unmanned aerial vehicle (UAV) is widely used to help communication network recently due to its maneuverability and flexibility. In this paper, we consider a UAV-assisted energy and spectrum harvesting (ESH) network to better solve the spectrum and energy scarcity problem, where nearby secondary users (SUs) harvest energy from the base station (BS) and perform data transmission to the BS, while remote SUs harvest energy from both BS and UAV but only transmit data to UAV to reduce the influence of near-far problem. We propose an unaligned time allocation scheme (UTAS) in which the uplink phase and downlink phase of nearby SUs and remote SUs are unaligned to achieve more flexible time schedule, including schemes (a) and (b) in remote SUs due to the half-duplex of energy harvesting circuit. In addition, maximum throughput optimization problems are formulated for nearby SUs and remote SUs respectively to find the optimal time allocation. The optimization problem can be divided into three cases according to the relationship between practical data volume and theoretical throughput to avoid the waste of time resource. The expressions of optimal energy harvesting time and data transmission time of each node are derived. Lastly, a successive convex approximation based iterative algorithm (SCAIA) is designed to get the optimal UAV trajectory in broadcast mode. Simulation results show that the proposed UTAS can achieve better performance than traditional time allocation schemes

Code-Folding Scheme for Energy Efficient Cooperative Communications in Wireless Sensor Networks

In order to provide high-efficiency monitoring for utilities consumption, a novel cooperative communication is investigated for Wireless Sensor Networks to overcome channel imperfections in an energy-efficient way. By matching dynamic network topologies with distributed graph network coding, a Code-Folding scheme is proposed to decrease the number of required cooperative terminals in ANCC. By converting original parallel bits transmission from a large number of nodes to serial packets transmission that relies on only a few nodes, The Code-Folding ANCC scheme can reform the cooperative scheme in an energy efficient way. Based on the Code-Folding ANCC scheme, Energy Efficient Cooperative Communications can achieve the same coding gain with a few sensor nodes, which can significantly improve transmission range, linkage reliability and energy efficiency. With experiments in an Energy Efficient Cooperative Communication testbed, the improvement of energy efficiency in the Energy Efficient Cooperative Communication network is verified

Rateless coding transmission over multi-state dying erasure channel for SATCOM

Abstract Satellite communication (SATCOM) systems have attracted great attention from academic and industrial communities in recent years, and huge amount of data delivery over satellite downlinks is considered as a promising service in emerging 5G networks, such as multimedia broadcasting. Nevertheless, due to intermittent connections from LEO or MEO satellite to earth station, and high dynamic channel conditions over downlinks, satellites may not be able to transmit the large data files to the ground station on time. In this paper, we propose a new rateless coding transmission for multi-state dying erasure channels (MDEC) with random channel life span and time-varying packet error rates, to improve the transmitting capability over SATCOM downlinks. Firstly, a heuristic approach for suboptimal degree distributions based on AND-OR tree technique is presented to achieve higher intermediate performance and lower symbol error rate of our proposed rateless codes. Furthermore, the appropriate code length of the connective window is derived and analyzed for enhanced average throughput on MDEC that is also optimized by maximum problem solving. Simulations have been conducted to evaluate the effectiveness of our rateless coding transmission for large file delivery on dynamic channel conditions. The results demonstrate that our proposed transmission scheme outperforms existing conventional rateless codes with significantly better intermediate performance and throughput performance over unreliable SATCOM downlinks, under time-varying packet error rates and unpredictable occurrences of exhausted energy or cosmic ray attacks

Deep Reinforcement Learning-Based Content Placement and Trajectory Design in Urban Cache-Enabled UAV Networks

Cache-enabled unmanned aerial vehicles (UAVs) have been envisioned as a promising technology for many applications in future urban wireless communication. However, to utilize UAVs properly is challenging due to limited endurance and storage capacity as well as the continuous roam of the mobile users. To meet the diversity of urban communication services, it is essential to exploit UAVs’ potential of mobility and storage resource. Toward this end, we consider an urban cache-enabled communication network where the UAVs serve mobile users with energy and cache capacity constraints. We formulate an optimization problem to maximize the sum achievable throughput in this system. To solve this problem, we propose a deep reinforcement learning-based joint content placement and trajectory design algorithm (DRL-JCT), whose progress can be divided into two stages: offline content placement stage and online user tracking stage. First, we present a link-based scheme to maximize the cache hit rate of all users’ file requirements under cache capacity constraint. The NP-hard problem is solved by approximation and convex optimization. Then, we leverage the Double Deep Q-Network (DDQN) to track mobile users online with their instantaneous two-dimensional coordinate under energy constraint. Numerical results show that our algorithm converges well after a small number of iterations. Compared with several benchmark schemes, our algorithm adapts to the dynamic conditions and provides significant performance in terms of sum achievable throughput