关键词: 超可靠低时延通信, 无人机, 多目标联合, 无人机能耗, 高斯过程回归

Abstract: Due to flexibility and high line-of-sight link probability, unmanned aerial vehicle (UAV) has been widely used in wireless communication systems. UAV is considered as air base station, and it can improve the coverage of the wireless communication system to the ground users as well as enhance the user rate. UAV is often powered by onboard batteries. Considering the weight and size of UAV, the capacity of onboard battery is very limited, which severely restricts the endurance of UAV. Therefore, in order to improve the continuous service of UAV to ground users in optimizing UAV-based applications, to reduce the energy consumption of UAV is one of the key issues that need to be solved. In addition, UAV often serves multiple users at the same time, and there are individual differences among these users, such as user traffic. Therefore, how to reasonably allocate resources to users according to user traffic, and then reduce energy consumption on the premise of meeting the user’ minimum speed requirements is an urgent problem to be solved in UAV-based applications. By solving this problem, the energy utilization rate of UAV can be improved, the endurance time of UAV can be extended, and the service continuity for users can be improved.
To this end, for scenarios where UAV serves multiple users, a power minimization algorithm of UAV based on traffic prediction of user (PMTP) is proposed. The PMTP algorithm controls UAV energy consumption from three aspects: resources allocation, transmission power control and UAV position. Firstly, user traffic is predicted by Gaussian process regression method, and the minimum user rate is calculated. This model is solved by optimizing UAV resources allocation, UAV transmission power and UAV position. The optimization problem is a mixed integer nonlinear programming problem, which relates three variables: UAV resources allocation, UAV transmission power and UAV position. Solving the optimal solution of this problem directly involves a lot of computation and high complexity.
Based on this, the suboptimal solution of the optimization problem is obtained by using block coordinate descending method. The original optimization problem is divided into three sub-problems and then solved step by step. Specifically, a sub-problem for optimizing resource allocation for a given transmission power and UAV location is first established. This sub-problem is an optimization problem of concave targets and linear constraints, which can be solved directly by using the CVXPY toolkit in Python. Then, taking the solution of resources allocation as a known condition, a sub-problem for optimizing transmission power for a given UAV location is established. This sub-problem is a convex optimization problem and can be solved directly. Finally, the obtained resources allocation and transmission power solutions are taken as known conditions to establish a sub-problem for optimizing UAV position. This sub-roblem is a convex optimization problem, which is solved by sequential deduction method.
Finally, the performance of the PMTP algorithm in predicting traffic accuracy, user average transmission rate and UAV energy consumption is analyzed through simulation experiments. The simulation results show that the Gaussian process regression method can accurately predict user traffic. The PMTP algorithm improves the average transmission rate of users. With a bandwidth of 50MHz, the average transmission rate for 10 users reaches 250Mbps. Compared with the maximum power and random algorithm, the PMTP algorithm reduces the energy consumption of UAV.
The PMTP algorithm uses Gaussian process regression to predict user traffic. Then, with the reliability of data transmission as the constraint condition, a target optimization problem is established, which includes UAV position, UAV transmission power and resources allocation. Finally, the block coordinate descending method is used. However, we only consider the scenario of one UAV. Later, the PMTP algorithm will be extended to adapt it to the multi-UAVS communication scenario, which will be the next research direction for this paper.

Key words: ultra-reliable low-latency communication, unmanned aerial vehicle, multi-objective joint, energy consumption of UAV, Gaussian process regression