In this paper, we consider the scenario where the base station (BS) transmits time-sensitive information to various users via shared orthogonal sub-channels. The BS allocates sub-channels and power to different users to complete the data transmission task in each time slot. The metric Age of Information (AoI) is adopted to measure the timeliness of information. We jointly optimize the transmitting decisions, the sub-channel assignments, and the power allocation schemes to minimize the average AoI of the users subject to the average and peak power constraints. We propose a dynamic scheduling policy based on the Lyapunov optimization method and then prove that the proposed policy can satisfy the power constraints and achieve a near-optimal AoI performance. Numerical simulations indicate that the proposed policy is stable and satisfies the power constraints, which validates the theoretical analysis.

This work considers a wireless network transmitting status updates arriving randomly using the Hybrid Automatic Repeat Request (HARQ) protocol under average power con-straints. We measure the information freshness by the Age of Synchronization, which is defined as the time elapsed since the last synchronization. The updates transmitted unsuccessfully can be retransmitted with a higher success probability by the HARQ. However, there is a trade-off between retransmitting the old update with a high success probability and transmitting the new update with a low success probability. We model the time-average AoS minimization problem as a constrained Markov decision process (CMDP) and solve the problem by a relative value iteration algorithm. Numerical simulations show that our scheduling policy outperforms traditional ARQ and HARQ policies on the AoS performance and indicates the stability of our scheduling policy under different channel parameters.

With the rapid development of the Internet of Things (IoT), a large amount of IoT devices are deployed to support various real-time applications, which put forward high requirements for data freshness. However, the limited battery capacity constraints the computation capabilities of terminal devices, thus decreases the timeliness of status updates. Mobile edge computing (MEC) has been considered as a solution to enhance the computing capacity of IoT devices and decrease their power consumption, thus extends their service life. In this paper, we aim to utilize MEC to decrease the power consumption of IoT devices while guaranteeing timeliness performance, which is measured by the metric age of information (AoI). We consider a multi-device scenario with time-varying channels and formulate the power-minimization problem with AoI constraints. Then we propose a dynamic scheduling policy based on Lyapunov optimization and analyze its performance theoretically. Numerical results validate the analysis and indicate that our policy outperforms the baselines.

This paper revisits the problem of sampling and transmitting status updates through a channel with random delay under a sampling frequency constraint. We use the Age of Information (AoI) to characterize the status information freshness at the receiver. The goal is to design a sampling policy that can minimize the average AoI when the statistics of delay is unknown. We reformulate the problem as the optimization of a renewal-reward process, and propose an online sampling strategy based on the Robbins-Monro algorithm. We prove that the proposed algorithm satisfies the sampling frequency constraint. Moreover, when the transmission delay is bounded and its distribution is absolutely continuous, the average AoI obtained by the proposed algorithm converges to the minimum AoI when the number of samples$K$goes to infinity with probability 1. We show that the optimality gap decays with rate$\mathcal {O}\left ({\ln K/K}\right)$, and the proposed algorithm is minimax rate optimal. Simulation results validate the performance of our proposed algorithm.

In this work, we study a status update system with a source node sending timely information to the destination through a channel with random delay. We measure the timeliness of the information stored at the receiver via the Age of Information (AoI), the time elapsed since the freshest sample stored at the receiver is generated. The goal is to design a sampling strategy that minimizes the total cost of the expected time average AoI and sampling cost in the absence of transmission delay statistics. We reformulate the total cost minimization problem as the optimization of a renewal-reward process, and propose an online sampling strategy based on the Robbins-Monro algorithm. Denote K to be the number of samples we have taken. We show that, when the transmission delay is bounded, the expected time average total cost obtained by the proposed online algorithm converges to the minimum cost when K goes to infinity, and the optimality gap decays with rate ${\mathcal{O}}$(ln K/K). Simulation results validate the performance of our proposed algorithm.

In this paper, we consider a multi-user multi-channel wireless network with a base station sending random fresh updates. To measure the data freshness of the network, the metric age of synchronization (AoS) is adopted. Our goal is to minimize the expected average AoS of the network. We first obtain a policy-independent lower bound via convex optimization and stochastic analysis under the perfect channel case, where all channels are available for users. Then we propose the perfect matching and maximum weight matching policies to approach the optimal performance. Under certain conditions, we prove theoretically that the gap between the lower bound and the expected average AoS under proposed policies vanishes as the number of channels goes to infinity. Numerical results validate the theoretical analysis and indicate that our policies can reach near-optimal performance.

In this paper, we consider a multi-user interference network where a central controller allocates power resources to multiple base stations for maximizing the entire network utility under a long-term convex power budget constraint. The optimal power allocation strategy depends on the accurate and instant channel state information (CSI). However, due to users’ mobility and the existence of channel fading and interference, timely channel estimation is unavailable. To overcome the difficulty of unknown channel states, we resort to the Lyapunov drift analysis framework and design an online power allocation algorithm based on historical CSI. The algorithm can be proven to achieve sub-linear performance for both cumulative regret and power budget violation. The sub-linear regret indicates the proposed algorithm can asymptotically achieve the optimal static power allocation performance in hindsight. Simulation results are provided to validate the asymptotic optimal performance of the proposed algorithm, as well as its robustness in the presence of adversarial interference.