论文标题
通过自适应Noma/OMA通过Multiuser计划优化信息新鲜度
Optimizing Information Freshness via Multiuser Scheduling with Adaptive NOMA/OMA
论文作者
论文摘要
本文考虑了一个带有基站(BS)的无线网络,该网络通过自适应非正交多访问(NOMA)/正交多访问(OMA)进行及时向多个客户端进行状态更新。具体而言,BS能够在Noma和OMA之间自适应切换以进行下行链路传输,以优化网络的新鲜度,其特征在于信息时代(AOI)度量。如果BS选择OMA,它只能在每个时间插槽内为一个客户提供服务,并且应该决定服务哪个客户;如果BS选择Noma,它可以同时为多个客户提供服务,并且需要决定分配给服务客户的权力。对于简单的两端情况,我们制定了马尔可夫决策过程(MDP)问题,并制定了BS决定是否基于两个客户的瞬时AOI来决定是否将Noma或OMA用于每个下行链路传输。最佳策略被证明具有具有明显决策切换边界的切换型属性。还设计了一个近乎最佳的计算复杂性策略。对于受建议的近乎最佳策略启发的更一般的多客户场景,我们通过在每个时间段中最大化网络的预期AOI降低来确定分配给每个客户的最佳功率的非线性优化问题。我们通过将其近似为凸优化问题来解决公式的问题。我们还得出了近似凸问题与原始非线性,非凸问题之间差距的上限。仿真结果验证了采用近似的有效性。通过求解凸优化,自适应Noma/OMA方案的性能显示与通过详尽的搜索解决的最大权重策略相似。
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...