[论文解读] Optimizing Information Freshness via Multiuser Scheduling with Adaptive NOMA/OMA

Qian Wang, He Chen+4 more|arXiv (Cornell University)|Jul 7, 2020

Age of Information Optimization参考文献 34被引用 10

一句话总结

本文提出了一种自适应NOMA/OMA调度方案，以优化多用户无线网络中的信息年龄（AoI）。通过为两个用户建立马尔可夫决策过程，并对多用户场景采用凸近似方法，该方案根据实时AoI动态选择NOMA或OMA，实现了接近最优的时效性，且复杂度较低，在高信噪比（SNR）和用户数量较多时相比OMA有显著性能增益。

ABSTRACT

This paper considers a wireless network with a base station (BS) conducting timely status updates to multiple clients via adaptive non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA). Specifically, the BS is able to adaptively switch between NOMA and OMA for the downlink transmission to optimize the information freshness of the network, characterized by the Age of Information (AoI) metric. If the BS chooses OMA, it can only serve one client within each time slot and should decide which client to serve; if the BS chooses NOMA, it can serve more than one client at the same time and needs to decide the power allocated to the served clients. For the simple two-client case, we formulate a Markov Decision Process (MDP) problem and develop the optimal policy for the BS to decide whether to use NOMA or OMA for each downlink transmission based on the instantaneous AoI of both clients. The optimal policy is shown to have a switching-type property with obvious decision switching boundaries. A near-optimal policy with lower computation complexity is also devised. For the more general multi-client scenario, inspired by the proposed near-optimal policy, we formulate a nonlinear optimization problem to determine the optimal power allocated to each client by maximizing the expected AoI drop of the network in each time slot. We resolve the formulated problem by approximating it as a convex optimization problem. We also derive the upper bound of the gap between the approximate convex problem and the original nonlinear, nonconvex problem. Simulation results validate the effectiveness of the adopted approximation. The performance of the adaptive NOMA/OMA scheme by solving the convex optimization is shown to be close to that of max-weight policy solved by exhaustive search...

研究动机与目标

• 解决多用户下行链路网络中NOMA与OMA之间缺乏自适应切换策略以最小化信息年龄（AoI）的问题。
• 设计一种基于用户瞬时AoI值的动态传输模式选择策略，实现NOMA与OMA之间的动态切换。
• 通过将非线性非凸优化问题近似为凸问题，为多用户场景设计一种低复杂度的近似最优策略。
• 建立近似优化问题与原始优化问题之间性能差距的理论边界。
• 在高信噪比和用户数量较多的情况下，显著优于传统OMA方案，实现AoI的显著降低。

提出的方法

• 为两用户系统建立马尔可夫决策过程（MDP），根据实时AoI状态确定NOMA/OMA切换的最优策略。
• 推导出基于AoI值的单调决策边界结构的切换型最优策略，实现高效策略计算。
• 通过利用MDP解的结构特性，提出一种计算复杂度更低的近似最优策略。
• 针对多用户系统，建立非线性非凸优化问题，通过功率分配最大化每时隙的期望AoI下降量。
• 将原始非凸问题近似为凸优化问题，以实现高效计算并提供可证明的性能边界。
• 推导出近似解与真实最优解之间差距的上界，表明其有界于 $ e^{-2} \sum_{k=1}^{K} w_{m(k)} \Delta_{m(k)} $。

实验结果

研究问题

• RQ1 基站应如何动态地在NOMA与OMA之间切换，以最小化长期平均加权和AoI？
• RQ2 在具有瞬时AoI反馈的两用户系统中，模式选择的最优策略具有何种结构？
• RQ3 能否设计一种低复杂度策略，在多用户场景下近似达到最优AoI性能？
• RQ4 在最大化AoI下降量方面，对非凸功率分配问题的凸近似方法有多准确？
• RQ5 凸近似解与真实最优解之间的理论性能差距是多少？

主要发现

• 两用户MDP的最优策略在AoI状态空间中表现出切换型结构，具有清晰的决策边界。
• 所提出的近似最优策略在性能上接近穷举搜索的加权最大值策略，但计算复杂度显著降低。
• 多用户功率分配问题的凸近似方法所得性能非常接近最优解，且其与最优解的差距有界，最大不超过 $ e^{-2} \sum_{k=1}^{K} w_{m(k)} \Delta_{m(k)} $。
• 自适应NOMA/OMA方案在高SNR区域和用户数量较多时，显著优于OMA方案。
• 仿真结果证实，自适应方案实现了显著的AoI降低，验证了所提近似方法与策略设计的有效性。
• 近似解与最优解之间性能差距的理论边界紧致且可显式量化。