Joint Placement Optimization and RNC in UAV-based Wireless Multicast Networks

TL;DR

This paper introduces an adaptive UAV-based network coding scheme that jointly optimizes UAV placement and packet scheduling to enhance multicast throughput in dynamic wireless networks with heterogeneous users.

Contribution

It proposes a novel joint optimization framework for UAV placement and RNC scheduling using greedy and PSO algorithms, addressing real-time multimedia delivery challenges.

Findings

The proposed scheme significantly improves multicast throughput.

UAV mobility effectively adapts to dynamic user distributions.

Simulation results validate the scheme's effectiveness.

Abstract

Random network coding (RNC) is an efficient coding scheme to improve the performance of the broadband networks, especially for multimedia applications which are popular in 5G network. However, it is a challenging work to transmit the real time media data because of the time limitation and wide band requirement. Moreover, the topology of the network changes due to users' movement, causing huge channel heterogeneity in large wireless network area. In this case, the fixed macro base station (BS) or access point may not fit the real-time user distributions. Accordingly, the UAV-based BS with high mobility can provide flexible service by adjusting it position according to users' locations to fit the dynamic topology of the network. Therefore, in this paper, we propose a UAV-based adaptive RNC (UARNC) scheme that jointly optimizes the UAV's location and RNC packet scheduling to maximize the throughput in a multicast network while guaranteeing the service quality of the bottleneck users. This problem is formulated as an optimization problem, and the greedy scheduling techniques and particle swarm optimization (PSO) algorithm are adopted to solve it. Finally, the simulation results prove the effectiveness of the proposed scheme.