Delay-Optimal Probabilistic Scheduling for Low-Complexity Wireless Links With Fixed Modulation and Coding: A Cross-Layer Design

A fundamental trade-off lies between the average queueing delay and the power consumption when transmitting stochastically arriving data over a fading channel. Providing real-time services on low-energy-supply and low-complexity devices has attracted much attention from both academia and industry, for its extensive use in transportation, manufacturing, living, and so on. In this paper, we investigate a joint channel-and-buffer-aware probabilistic scheduling policy, which is generalized from conventional deterministic scheduling, in order to optimize the delay-power trade-off without paying the cost of high complexity. In particular, we focus on communication systems with a given fixed modulation and coding scheme, where power adaptation is adopted to compensate the bad channel state. We formulate a Markov reward process (MRP) to analyze the average delay and power consumption, based on which we minimize the average delay, given a certain power constraint via linear programming (LP). By solving the LP problem, optimal delay-power trade-off curves are obtained under a threshold-based scheduling policy.