[论文解读] Throughput Maximization for Mobile Relaying Systems
本文提出了一种在移动中继系统中联合优化发射功率与中继轨迹的方法,以最大化吞吐量,利用解码-存储-转发(DSF)策略并引入信息因果性约束。结果表明,通过阶梯式注水水填法优化功率分配以及高速中继移动,相较于静态中继,可显著提升吞吐量,尤其在延迟容忍场景下优势明显。
Relaying is an effective technique to achieve reliable wireless connectivity in harsh communication environment. However, most of the existing relaying schemes are based on relays with fixed locations, or \emph{static relaying}. In this paper, we consider a novel \emph{mobile relaying} technique, where the relay nodes are assumed to be capable of moving at high speed. Compared to static relaying, mobile relaying offers a new degree of freedom for performance enhancement via careful relay trajectory design. We study the throughput maximization problem in mobile relaying systems by optimizing the source/relay transmit power along with the relay trajectory, subject to practical mobility constraints (on the relay speed and initial/final relay locations), as well as the \emph{information-causality constraint} at the relay owing to its decode-store-and-forward (DSF) strategy. It is shown that for fixed relay trajectory, the throughput-optimal source/relay power allocations over time follow a "staircase" water filling (WF) structure, with \emph{non-increasing} and \emph{non-decreasing} water levels at the source and relay, respectively. On the other hand, with given power allocations, the throughput can be further improved by optimizing the relay trajectory via successive convex optimization. An iterative algorithm is thus proposed to optimize the power allocations and relay trajectory alternately. Furthermore, for the special case with free initial and final relay locations, the jointly optimal power allocation and relay trajectory are derived. Numerical results show that by optimizing the trajectory of the relay and power allocations adaptive to its induced channel variation, mobile relaying is able to achieve significant throughput gains over the conventional static relaying.
研究动机与目标
- 为解决动态环境中静态中继的吞吐量限制问题,通过高移动性中继实现通信性能提升。
- 在实际移动性与信息因果性约束下,联合优化中继轨迹与源/中继功率分配。
- 构建一个可处理的移动中继框架,以利用中继移动引起的信道变化。
- 证明经优化轨迹与功率控制的移动中继优于传统静态中继。
提出的方法
- 建立一个三维移动中继系统模型,其中高速中继在固定的源节点与目的节点之间移动。
- 构建一个吞吐量最大化问题,约束条件包括中继速度、初始/最终位置,以及由于DSF操作引起的因果性信息约束。
- 推导出最优功率分配为一种‘阶梯式’注水结构,其中源端注水水平非递增,中继端注水水平非递减。
- 采用连续凸优化方法,通过交替优化中继轨迹与功率分配,迭代地改进解。
- 证明当初始/最终位置自由时,联合最优解具有非递减的中继轨迹,并实现恒定速度移动。
- 通过凸近似获得可达速率的下界,以支持高效迭代优化。
实验结果
研究问题
- RQ1与静态中继相比,优化中继轨迹在移动中继系统中如何提升吞吐量?
- RQ2当中继轨迹固定时,在解码-存储-转发(DSF)协议下,最优的时间域功率分配策略是什么?
- RQ3联合优化功率分配与中继轨迹是否能获得高于分别优化的吞吐量?
- RQ4在移动性与信息因果性约束下,最优中继轨迹具有何种结构特性?
- RQ5与即时转发相比,信息因果性约束如何影响移动中继系统的设计?
主要发现
- 对于固定中继轨迹,最优功率分配遵循‘阶梯式’注水结构,源端注水水平非递增,中继端注水水平非递减。
- 移动中继带来的吞吐量增益显著——数值结果表明,通过利用移动引起的信道变化,可实现相对于静态中继的显著性能提升。
- 在初始与最终中继位置自由的特殊情况下,最优轨迹为非递减,并在非边界区域实现恒定速度移动。
- 证明最优中继轨迹在源与目的之间时具有恒定速度段(等于最大速度),在端点处速度为零。
- 基于连续凸优化的迭代算法可收敛至联合优化功率与轨迹的解,性能优于分别优化。
- 信息因果性约束在移动中继中至关重要,因为缓冲机制要求中继必须在接收完成后才能转发数据,因此需仔细调度传输与移动过程。
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