PhD Defence Notice - Weisen Shi

Tuesday, May 5, 2020 10:00 am - 10:00 am EDT (GMT -04:00)

Candidate: Weisen Shi

Title: Multi-drone-cell 3D Trajectory Planning and Resource Allocation for Drone-assisted Radio Access Networks

Date: May 5, 2020

Time: 10:00 AM


Supervisor(s): Shen, Sherman


Equipped with communication modules, drones can perform as drone-cells (DCs) that provide on-demand communication services to terrestrial users in various scenarios, such as remote area monitoring, post-disaster communication recovery, Internet of things (IoT) data collections, and temporal communication provisioning. As the aerial relay nodes between terrestrial users and base stations (BSs), DCs are leveraged to extend wireless connections for uncovered users of radio access networks (RAN), which forms the drone assisted RAN (DA-RAN). In DA-RAN, the communication coverage, quality-of-service (QoS) performance and deployment flexibility can be improved by DCs due to their line-of-sight drone-to-ground (D2G) wireless links and the dynamic deployment capabilities. Considering the special mobility pattern, channel model, energy consumption, and other features of DCs, it is essential yet challenging to design the flying trajectories and resource allocation schemes for DA-RAN. In specific, given the emerging D2G communication models and dynamic deployment capability of DCs, new DC deployment strategies are required by DA-RAN instead of the conventional cell deployment strategies for terrestrial RAN. Moreover, to exploit the fully controlled mobility of DCs and promote the user fairness, the flying trajectories of DCs and the D2G communications must be jointly optimized. Further, to serve the high-mobility users (e.g. vehicular users) whose mobility patterns are hard to be modeled, both the trajectory planning and resource allocation schemes for DA-RAN should be re-designed to adapt to the variations of terrestrial traffic. To address the above challenges, in this thesis, we propose a DA-RAN architecture in which multiple DCs are leveraged to relay data between BSs and terrestrial users. Based on the theoretical analyses of the D2G communication, DC energy consumption, and DC mobility features, the deployment, trajectory planning and communication resource allocation of multiple DCs are jointly investigated for both quasi-static and high-mobility users.

We first analyze the communication coverage, drone-to-BS (D2B) backhaul link quality, and optimal flying height of the DC according to the state-of-the-art drone-to-user (D2U) and D2B channel models. We then formulate the multi-DC three-dimensional (3D) deployment problem with the objective of maximizing the ratio of effectively covered users while guaranteeing D2B link qualities. To solve the problem, a per-drone iterated particle swarm optimization (DI-PSO) algorithm is proposed, which prevents the large particle searching space and the high violating probability of constraints existing in the pure PSO based algorithm. Simulations show that the DI-PSO algorithm can achieve higher coverage ratio with less complexity comparing to the pure PSO based algorithm.

Secondly, to improve overall network performance and the fairness among edge and central users, we design 3D trajectories for multiple DCs in DA-RAN. The multi-DC 3D trajectory planning and scheduling is formulated as a mixed integer non-linear programming (MINLP) problem with the objective of maximizing the average D2U throughput. To address the non-convexity and NP-hardness of the MINLP problem due to the 3D trajectory, we first decouple the MINLP problem into multiple integer linear programming and quasi-convex sub-problems in which user association, D2U communication scheduling, horizontal trajectories and flying heights of DBSs are respectively optimized. Then, we design a multi-DC 3D trajectory planning and scheduling algorithm to solve the sub-problems iteratively based on the block coordinate descent (BCD) method. A k-means-based initial trajectory generation scheme and a search-based start slot scheduling scheme are also designed to improve network performance and control mutual interference between DCs, respectively. Compared with the static DBS deployment, the proposed trajectory planning scheme can achieve much lower average value and standard deviation of D2U pathloss, which indicate the improvements of network throughput and user fairness.

Thirdly, considering the highly dynamic and uncertain environment composed by high mobility users, we propose a hierarchical deep reinforcement learning (DRL) based multi DC trajectory planning and resource allocation (HDRL-TPRA) scheme for high-mobility users, which maximizes the accumulative network throughput when satisfying user fairness, DC power consumption and DC-to-ground link quality constraints. To address the high dynamics and uncertainties of high-mobility environment, we first decouple the multi-DC TPRA problem into two hierarchical sub-problems, i.e., the higher-level global trajectory planning sub-problem and the lower-level local TPRA sub-problem. The global trajectory planning sub-problem roughly plans trajectories for multiple DCs over a long time period in a large area. A multi-agent DRL based global trajectory planning (MARL-GTP) algorithm is proposed to solve it, which learns the multi-DC trajectory planning policy to maximize the accumulative number of users being served. The non-stationary state space caused by multi-DC environment is addressed by the multi-agent fingerprint technique. The local TPRA sub-problem controls the real-time movement of single DC and resource allocation within its communication coverage. Given the global trajectory planning results, a deep deterministic policy gradient based LTPRA (DDPG-LTPRA) algorithm executed on each DC is proposed for the lower-level sub-problem, which independently adjust the DC movements and allocate transmit power according to the real-time user traffic variations. The HDRL-TPRA scheme is composed by the two algorithms solving both sub-problems, respectively. The real-world based simulations show the performance improvements provided by the proposed HDRL-TPRA scheme over the non-learning-based TPRA scheme.

In summary, we have investigated the multi-DC 3D deployment, trajectory planning and communication resource allocation in DA-RAN considering different user mobility patterns in this thesis. The proposed schemes and theoretical results should provide useful guidelines for future research in DC trajectory planning, resource allocation, as well as the real deployment of DCs in complex environments with diversified users.