To enhance the message exchange rate between ship1 (S1) and ship2 (S2), an intelligent reflective surface (IRS)-and-unmanned aerial vehicle (UAV)-assisted two-way amplify-and-forward (AF) relay maritime communication network (MCN) is proposed, where S1 and S2 communicate each other via a UAV-mounted IRS and an AF relay. Besides, an optimization 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 put forward. 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 MCN via the proposed LC-ZF-SCA and ONS-SDP-PSCA methods surpass those of with random phase and only AF relay.

翻译：为提升船1（S1）与船2（S2）之间的消息交换速率，提出了一种智能反射面（IRS）与无人机（UAV）辅助的双向放大转发（AF）中继海事通信网络（MCN），其中S1与S2通过搭载IRS的UAV和AF中继进行通信。此外，构建了一个最大化最小速率的优化问题，需对变量——即两个时隙的AF中继波束成形矩阵与IRS相位偏移——进行优化。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。为获得显著的速率提升，提出了一种基于单步、半定松弛与惩罚SCA的高性能交替迭代（ONS-SDP-PSCA）方法。仿真结果表明，通过所提出的LC-ZF-SCA与ONS-SDP-PSCA方法，IRS与UAV辅助的AF中继MCN的速率超越了随机相位及仅采用AF中继的方案。