Optimizing energy efficiency for supporting near-cloud access region of UAV based NOMA networks in IoT systems

Non-orthogonal multiple access (NOMA) and unmanned aerial vehicle (UAV) are two promising technologies for wireless the fifth generation (5G) networks and beyond. On one hand, UAVs can be deployed as flying base stations to build line-of-sight (LoS) communication links to two ground users (GUs) and to improve the performance of conventional terrestrial cellular networks. On the other hand, NOMA enables the share of an orthogonal resource to multiple users simultaneously, thus improving the spectral efficiency and supporting massive connectivities. This paper presents two protocols namely cloud-base central station (CCS) based power-splitting protocol (PSR) and time-switching protocol (TSR), for simultaneously wireless information and power transmission (SWIPT) at UAV employed in power domain NOMA based multi-tier heterogeneous cloud radio access network (H-CRAN) of internet of things (IoT) system. The system model with k types of UAVs and two users in which the CCS manages the entire H-CRAN and operates as a central unit in the cloud is proposed in our work. Closed-form expressions of throughput and energy efficiency (EE) for UAVs are derived. In particular, the EE is determined for the impacts of power allocation at CCS, various UAV types and channel environment. The simulation results show that the performance for CCS-based PSR outperforms that for CCS-based TSR for the impacts of power allocation at the CCS. On contrary, the TSR protocol has a higher EE than the PSR in cases of the impact of various UAV types and channel environment. The analytic results match Monte Carlo simulations

A review of relay network on UAVS for enhanced connectivity

One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented

Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.Comment: 15 pages, 10 figures, 2 algorithm