483 research outputs found
Communications with spectrum sharing in 5g networks via drone-mounted base stations
The fifth generation wireless network is designed to accommodate enormous traffic demands for the next decade and to satisfy varying quality of service for different users. Drone-mounted base stations (DBSs) characterized by high mobility and low cost intrinsic attributes can be deployed to enhance the network capacity. In-band full-duplex (IBFD) is a promising technology for future wireless communications that can potentially enhance the spectrum efficiency and the throughput capacity. Therefore, the following issues have been identified and investigated in this dissertation in order to achieve high spectrum efficiency and high user quality of service.
First, the problem of deploying DBSs is studied. Deploying more DBSs may increase the total throughput of the network but at the expense of the operation cost. The droNe-mounted bAse station PlacEment (NAPE) problem with consideration of IBFD communications and DBS backhaul is then formulated. The objective is to minimize the number of deployed DBSs while maximizing the total throughput of the network by incorporating IBFD-enabled communications for both access links and backhaul links via DBSs as relay nodes. A heuristic algorithm is proposed to solve the NAPE problem, and its performance is evaluated via extensive simulations.
Second, the 3-D DBS placement problem is investigated as the communication efficiency is greatly affected by the positions of DBSs. Then, the DBS placement with IBFD communications (DSP-IBFD) problem for downlink communications is formulated, and two heuristic algorithms are proposed to solve the DSP-IBFD problem based on different DBS placement strategies. The performance of the proposed algorithms are demonstrated via extensive simulations.
Third, the potential benefits of jointly optimizing the radio resource assignment and 3-D DBS placement are explored, upon which the Drone-mounted Base Station Placement with IBFD communications (DBSP-IBFD) problem is formulated. Since the DBSP-IBFD problem is NP-hard, it is then decomposed into two sub-problems: the joint bandwidth, power allocation and UE association problem and the DBS placement problem. A 1/2(1-/2^{l}})-approximation algorithm is proposed to solve the DBSP-IBFD problem based on the solutions to the two sub-problems, where l is the number of simulation runs. Simulation results demonstrate that the throughput of the proposed approximation algorithm is superior to benchmark algorithms.
Fourth, the uplink communications is studied as the mobile users need to transmit and receive data to and from base stations. The Backhaul-aware Uplink communications in a full-duplex DBS-aided HetNet (BUD) problem is investigated with the objective to maximize the total throughput of the network while minimizing the number of deployed DBSs. Since the BUD problem is NP-hard, it is then decomposed into three sub-problems: the joint UE association, power and bandwidth assignment problem, the DBS placement problem and the problem of determining the number of DBSs to be deployed. The AA-BUD algorithm is proposed to solve the BUD problem with guaranteed performance based on the solutions to the three sub-problems, and its performance is demonstrated via extensive simulations.
The future work comprises two parts. First, a DBS can be used to provide both communications and computing services to users. Thus, how to minimize the average latency of all users in a DBS-aided mobile edge computing network requires further investigation. Second, the short flying time of a drone limits the deployment and the performance of DBSs. Free space optics (FSO) can be utilized as the backhaul link and the energizer to provision both communication and energy to a DBS. How to optimize the charging efficiency while maximizing the total throughput of the network requires further investigation
A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions
The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network
- …