Abstract
Recent years, unmanned aerial vehicles (UAVs) have attracted much attention for providing intermediate relay to ground mobile user equipments (UEs) for their flexible mobility. UEs can offload computing-intensive task to mobile cloud computing (MCC) or mobile edge computing (MEC) for fast processing. However, with multi-UAV and ground mobile UEs in the system, heterogeneous performance requirement as well as fast-changing communication condition make the system more complicated. Meanwhile, both UEs and UAVs are battery-driven. How to optimize the energy efficiency for UEs' transmission and UAVs' position should be carefully considered. Since this is a non-convex and mixed-integer optimization problem, a heuristic joint power and quality of experience (HJPQ) algorithm is proposed in this article, where the UEs' offloading delay, MIMO channel, transmission power, as well as UAVs' placement are jointly optimized. The numeral simulations not only reveal the effectiveness of HJPQ, but also guarantee the great quality of experience (QoE) performance for UEs with different priorities. Furthermore, the comparison experiments with random assignment and deep deterministic policy gradient (DDPG) show the superiority of HJPQ in lower complexity, faster convergence, shorter offloading delay as well as higher energy efficiency.
Highlights
With the rapid development of ground mobile user equipments (UEs), the data traffic has been witnessed an exponential growth in recent years, which can provide a powerful platform to various applications
It has been shown that short-distance line-of-sight (LoS) communication links between Unmanned aerial vehicles (UAVs) and ground UEs can be efficiently exploited in multi-UAV assisted wireless networks for performance enhancement by location assignment, and UAVs can fly close to the edge UEs who are far away from ground base station (GBS) or noneline-of sight caused by terrain to provide offloading relay service
Despite of the extensive researches and applications in UAV assisted wireless communication networks, few literature refers to the UAVs placement, channel allocation as well as UEs scheduling with the comprehensively consideration of system quality of experience (QoE) requirement such as delay, throughput and energy efficiency
Summary
With the rapid development of ground mobile user equipments (UEs), the data traffic has been witnessed an exponential growth in recent years, which can provide a powerful platform to various applications. It has been shown that short-distance line-of-sight (LoS) communication links between UAVs and ground UEs can be efficiently exploited in multi-UAV assisted wireless networks for performance enhancement by location assignment, and UAVs can fly close to the edge UEs who are far away from ground base station (GBS) or noneline-of sight caused by terrain to provide offloading relay service. 2) Energy efficiency related to system endurance should be considered for both UAVs and mobile UEs in the offloading-and-relay scenario [12] For the former, UAVs are generally battery-driven and tend to move to a ‘‘better’’ position to improve the channel condition and enhance the transmission rate. The major contributions of the paper are shown as follows: 1) A multi-UAV assisted communication model related to offloading delay and energy efficiency is established.
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