Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks (1801.01803v1)

Abstract: We consider a wireless broadcast network with a base station sending time-sensitive information to a number of clients through unreliable channels. The Age of Information (AoI), namely the amount of time that elapsed since the most recently delivered packet was generated, captures the freshness of the information. We formulate a discrete-time decision problem to find a transmission scheduling policy that minimizes the expected weighted sum AoI of the clients in the network. We first show that in symmetric networks a Greedy policy, which transmits the packet with highest current age, is optimal. For general networks, we develop three low-complexity scheduling policies: a randomized policy, a Max-Weight policy and a Whittle's Index policy, and derive performance guarantees as a function of the network configuration. To the best of our knowledge, this is the first work to derive performance guarantees for scheduling policies that attempt to minimize AoI in wireless networks with unreliable channels. Numerical results show that both Max-Weight and Whittle's Index policies outperform the other scheduling policies in every configuration simulated, and achieve near optimal performance.

• The paper demonstrates that a Greedy policy optimally minimizes AoI in symmetric networks with identical channel conditions.
• The paper introduces three low-complexity scheduling policies—Randomized, Max-Weight, and Whittle's Index—to address diverse network configurations.
• The paper shows that simulation results validate near-optimal performance for the Max-Weight and Whittle’s Index policies across various settings.

Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks

This paper addresses the problem of minimizing Age of Information (AoI) in a wireless broadcast network. The focus is on scenarios where a base station transmits time-sensitive information over unreliable channels to multiple clients. AoI is a metric that captures the freshness of information delivered to a client and is critical for applications involving time-sensitive data such as sensor networks or command-control systems.

Main Contributions

1. Greedy Policy Optimality for Symmetric Networks: The authors establish that a Greedy policy, which prioritizes transmission to clients with the highest current AoI, is optimal for symmetric networks—networks where clients have identical channel reliability and importance weights.
2. Low-Complexity Scheduling Policies: For general networks, where clients may have different channel reliabilities and importance weights, the paper introduces three low-complexity scheduling policies: a Randomized policy, a Max-Weight policy, and a Whittle's Index policy. Each policy is designed to perform well under varying network configurations.
3. Performance Analysis and Guarantees: The paper provides performance guarantees for these policies. For instance, the Max-Weight and Whittle’s Index policies are shown to outperform others in simulations, achieving near-optimal results under various configurations.

Numerical Results and Claims

The numerical simulations demonstrate that both the Max-Weight and Whittle’s Index policies consistently outperform the Randomized and Greedy policies, achieving performance close to the optimal Dynamic Programming solution across all tested configurations. This finding supports the efficacy of these policies in providing robust AoI minimization over unreliable channels.

Implications

The practical implication of these findings lies in the potential improvements in network performance for a variety of applications where timely information updates are crucial. Theoretically, this paper extends our understanding of the AoI optimization problem, providing insights into designing efficient scheduling policies for more generalized network setups.

Future Directions

The research opens avenues for further exploration of scheduling policies in more complex scenarios such as multi-hop networks, networks with stochastic arrivals, or those with time-varying channels. Additionally, future research could investigate more adaptive methods that dynamically adjust to changing network conditions and priorities.

In summary, the paper provides a thorough investigation of AoI optimization in wireless networks, presenting novel scheduling strategies and comprehensive performance analyses. These contributions significantly enhance our understanding of maintaining information freshness in broadcast wireless networks.