Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Restless Multi-Armed Bandit Scheduling | 1-D Bandit | Regret (Reg(T))1.19e+4 | 15 | |
| Restless Multi-Armed Bandit Scheduling | Wireless Scheduling Synthetic | Cumulative Regret2.60e+3 | 15 | |
| Restless Multi-Armed Bandit Scheduling | Wireless Scheduling Real | Cumulative Regret (T)1.05e+4 | 10 | |
| Robot state prediction | Robot state prediction instance | MSE11.44 | 7 | |
| Hand pose prediction | Hand pose prediction 3-modality | L2 Error (cm)3.24 | 6 | |
| Vector autoregressive process prediction | Vector autoregressive process | MSE13.3 | 6 |