近年来随着移动通信技术飞速发展，物联网、智慧交通、自动驾驶等实时应用应运而生。在这些应用中，海量终端传感器采集路况信息、车辆位置等实时数据并进行处理，将得到的状态信息更新上传到指挥中心综合处理以辅助调度决策，随后基站将信息推送给相应用户以满足不同应用的需求。这些状态更新业务对信息的时效性提出了较高的要求，而传统的传输速率、网络时延等通信指标难以精确刻画信息的时效性。为此，研究者们提出了信息年龄、同步年龄等指标作为时效性的度量。此外，设备接入量大导致通信资源不足，难以及时传输所有数据，需要设计合理的资源分配与调度策略。基于上述问题，本文针对信息获取、处理与传输过程面临的需求与挑战，以信息年龄和同步年龄为指标，开展通信资源分配与调度策略研究以优化网络的信息时效性。本文主要工作包括：针对实时数据获取与处理过程，研究边缘信息获取与处理的时效性与能耗优化调度策略。本文考虑信道统计信息未知的挑战，提出了一种基于李雅普诺夫优化的动态调度策略，动态决定各终端设备的采样时刻与信息处理方式。通过理论证明和数值仿真可知，所提策略可以满足不同设备的信息年龄需求，同时设备平均能耗可以逼近理论最优值。针对实时数据推送过程，研究基站端功率约束下信息年龄最小化的调度策略。本文将优化问题建模为受限马尔可夫决策过程，并提出了一种基于线性规划求解的稳态随机调度策略以最小化用户平均信息年龄。通过理论证明和数值仿真可知，所提策略在大规模网络中具有渐近最优性。在上述研究基础上，进一步考虑功率与子信道联合分配，本文提出了基于李雅普诺夫优化的动态资源分配与调度策略，为各用户动态分配子信道与传输功率。通过理论证明和数值仿真可知，所提策略可以满足基站端功率约束，且用户平均信息年龄可以逼近理论最优值。针对实时数据同步过程，本文以同步年龄为时效性指标，研究同步年龄最小化的基站缓存设置与信道分配策略。在缓存设置研究方面，本文基于惠特尔索引提出了无缓存网络最优调度策略并与其他缓存设置网络进行性能对比，探究不同缓存容量和设置对网络信息时效性的影响。在信道分配策略研究方面，本文提出了基于最大权匹配算法的信道分配策略，并通过理论分析与数值仿真证明了所提策略在大规模网络中的渐近最优性。

In recent years, the rapid development of mobile communication has led to the rise of real-time applications such as the Internet of Things, intelligent transportation, and automatic driving. In these applications, a large number of terminal sensors collect and process real-time data such as road conditions, position of vehicles, etc. Then the base station transmits the information to corresponding users to meet the needs of different applications. These state update applications put high requirements for data freshness, which can not be accurately measured by traditional metrics such as data rate and transmission delay. Therefore, the researchers proposed the metrics Age of Information (AoI) and Age of Synchronization (AoS) to measure the data freshness. In addition, due to the constraints of communication resources, it is difficult to transmit all data in time. To handle the problem, we study the scheduling and resource allocation policies to optimize the AoI and AoS performance of wireless communication networks. The main contributions of our work are the following: Considering the scenario of real-time data collection and processing, we study the scheduling policy to optimize the information timeliness and power consumption of terminal devices. Considering the challenge of unknown channel statistics, we propose a dynamic scheduling strategy based on Lyapunov optimization to determine the sampling time and way of information processing for terminal devices. Theoretical analysis and numerical simulations show that the proposed policy can satisfy the AoI requirement of different devices, and the average power consumption of the device can achieve a near-optimal performance.Considering the scenario of real-time data transmission, we study the scheduling policy to minimize the average AoI of users under the power constraints of the base station. We formulate the optimization problem as a constrained Markov decision process and propose a stationary randomized scheduling policy based on linear programming to minimize the average AoI of users. Theoretical analysis and numerical simulations show that the proposed policy is asymptotically optimal with the increase in the number of users. What‘s more, considering the joint allocation of power and sub-channels, we propose a dynamic resource allocation and scheduling policy to dynamically allocate sub-channels and power to users. Theoretical analysis and numerical simulations show that the proposed policy can satisfy the power constraints of the base station and reach a near-optimal average AoI performance.Considering the scenario of real-time data synchronization, we use the metric AoS as the measurement of information timeliness since AoS is more accurate than AoI in this scenario. In terms of the research on cache setting, we propose a scheduling policy based on Whittle‘s index for the no-buffer network. Then we compare the AoS performance of the no-buffer network and buffered network to study the impact of different buffer settings on the timeliness performance of the network. In terms of the research on the channel allocation policy, we propose a channel allocation policy based on the Max-weight matching. Theoretical analysis and numerical simulations indicate that the proposed policy is asymptotically optimal.