Adaptive Packet Length Adjustment for Minimizing Age of Information Over Fading Channels

Motivated by the high information freshness requirement in the Internet of Things (IoT), this paper investigates adaptive packet length adjustment schemes to minimize the average age of information (AoI) for status update systems, where a machine type communication device adaptively adjusts the packet length in real time by exploiting the channel state information and AoI. Since the status packets in the IoT are often short, a significant packet error rate is introduced. Due to the instability of channel fading and the high packet error rate, optimizing the AoI performance is tricky. Under a power consumption constraint, the AoI minimization problem is modeled as a constrained Markov decision process (CMDP), and the structure of the optimal scheme is revealed. Then, under the CMDP framework, this paper transforms the AoI minimization problem into a linear programming problem and proposes a probabilistic packet length adjustment scheme, which can lead to the optimal solution. When the power consumption constraint is loose, a low-complexity suboptimal scheme is further proposed, where the expected average AoI of one period length is minimized. Simulation results verify the superiority of the proposed optimal scheme and show that the proposed low-complexity scheme can reach near-optimal performance.