Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization

Abstract

In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems.

3D Placement Optimization in UAV-Enabled Communications: A Systematic Mapping Study

“…• Data throughput Data throughput is the transmitted data over a communication channel per unit of time. A vast majority (53.3%) of analyzed works (i.e., [62]- [64], [66], [68], [70]- [75], [78], [79], [81]- [87], [89]- [96], [98]- [102], [104]- [111]) focuses on data throughput. All the studies above aim to maximize data throughput by optimizing the placement of UAVs.…”

“…Additionally, Huang [64] utilized the semi-definite relaxation technique to optimize UAV horizontal placement and transmission power. Interestingly, around 3% of the contributions have used exact and heuristic algorithms, e.g., [104], [106], [112], [144]. For instance, Zhang et al [112] optimized bandwidth allocation and user association using the Karush-Kuhn-Tucker conditions and employed heuristic algorithms to find the optimal 3D placement.…”

Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

“…Likewise, in [4], the UAV is deployed as a relay in a device-to-device cellular system, where the objective of the UAV is to assist the communication link between nodes that suffer from direct communication links. In [5], we consider a drone that is deployed as an independent multiple-input multiple-output (MIMO) BS with full-duplex (FD) capabilities to support the communication needs of the FD ground users. Similarly, the authors in [6] studied multiple UAV-enabled multi-user communication models, where the altitude and antenna beamwidth of the UAV were optimized to improve the throughput.…”