In this paper, an intelligent reflecting surface (IRS)-and-unmanned aerial vehicle (UAV)-assisted two-way amplify-and-forward (AF) relay network in maritime Internet of Things (IoT) is proposed, where ship1 (S1) and ship2 (S2) can be viewed as data collecting centers. To enhance the message exchange rate between S1 and S2, a problem of maximizing minimum rate is cast, where the variables, namely AF relay beamforming matrix and IRS phase shifts of two time slots, need to be optimized. To achieve a maximum rate, a low-complexity alternately iterative (AI) scheme based on zero forcing and successive convex approximation (LC-ZF-SCA) algorithm is presented. To obtain a significant rate enhancement, a high-performance AI method based on one step, semidefinite programming and penalty SCA (ONS-SDP-PSCA) is proposed. Simulation results present the rate of the IRS-and-UAV-assisted AF relay network via the proposed LC-ZF-SCA and ONS-SDP-PSCA methods surpass those of with random phase and only AF relay.

翻译：本文提出了一种面向海事物联网（IoT）的智能反射面（IRS）与无人机（UAV）辅助的双向放大转发（AF）中继网络，其中船1（S1）和船2（S2）可视为数据汇聚中心。为提升S1与S2之间的信息交换速率，本文构建了一个最大化最小速率的问题，需优化AF中继波束成形矩阵及两个时隙的IRS相移变量。为实现最大速率，提出了一种基于迫零与逐次凸逼近的低复杂度交替迭代（LC-ZF-SCA）算法。为进一步获得显著的速率提升，提出了一种基于单步、半定规划与惩罚SCA的高性能AI方法（ONS-SDP-PSCA）。仿真结果表明，所提出的LC-ZF-SCA与ONS-SDP-PSCA方法下IRS与无人机辅助AF中继网络的速率均超越了随机相位及仅使用AF中继的方案。