Delay-Optimal Transmission Scheduling Policies for Time-Correlated Fading Channels

Abstract

Millimeter-wave (mmWave) networks have the potential to support high throughput and low-latency requirements of 5G-and-beyond communication standards. But transmissions in this band are highly vulnerable to attenuation and blockages from humans, buildings, and foliage, which increase end-to-end packet delays. This work designs dynamic scheduling policies that minimize end-to-end packet delays while keeping packet transmission costs low. Specifically, we consider a mmWave network that consists of a transmitter that transmits data packets over an unreliable communication channel modeled as a Gilbert-Elliott channel.The transmitter operates under an ACK/NACK feedback model and does not observe the channel state unless it attempts a transmission. The objective is to minimize a weighted average cost consisting of end-to-end packet delays and packet transmission costs. We pose this dynamic optimization problem as a partially observable Markov decision process (POMDP). To the best of our knowledge, this is the first POMDP formulation for mmWave network with partial channel state information that considers delay minimization. We show that the POMDP admits a solution that has a threshold structure, i.e., for each queue length, the belief (the conditional probability that the channel is in a good state) is partitioned into intervals, and the transmitter sends j packets when the belief lies in the j-th interval. We then consider the case when the system parameters such as the packet arrival rate, and the transition probabilities of the channel are not known, and leverage these structural results in order to use the actor-critic algorithm to efficiently search for a policy that is locally optimal.