Throughput Maximization with an Average Age of Information Constraint in Fading Channels

In fifth generation and beyond networks, communication nodes are expected to support two crucial classes of information traffic, namely, enhanced mobile broadband (eMBB) traffic with high data rate requirements and ultra-reliable low-latency communications (URLLC) traffic with strict requirements on latency and reliability. URLLC traffic, which is usually analyzed by a metric called the age of information (AoI), is assigned first priority over the resources at a node. Motivated by this, we consider a long-term average throughput maximization problem subject to average AoI and power constraints in a single user fading channel, when perfect channel state information at the transmitter (CSIT) is available. The problem considered is a constrained Markov decision process with an unbounded immediate cost. Noting that obtaining its solution is either impossible or computationally prohibitive using dynamic programming, we propose a simple age-independent stationary randomized policy (AI-SRP), which allocates power at the transmitter based only on the channel state and distribution information, without any knowledge of the AoI. We show that the AI-SRP is near-optimal in the sense that the optimal throughput achieved by it is at least half of the optimal long-term average throughput, for any problem parameters, and that it is within an additive gap, expressed in terms of the optimal dual variable corresponding to its average AoI constraint, from the optimal long-term average throughput.