Energy-Efficient Distributed Transmission Scheme for MTC in Dense Wireless Networks: A Mean-Field Approach

Abstract

In this article, we address the problem of long-term resource allocation for a 5G and beyond dense wireless network. Of particular interest within the considered framework are machine-type communications (MTCs). To establish an optimal operation for an energy-efficient MTC system, the power allocation policy should be designed by taking into consideration both channel and queue states of the devices. This problem formulation belongs to the category of complex stochastic optimization that can be recast as a Markov decision process (MDP) over high-dimensional state space. We circumvent the hurdle inherent to state space of a huge dimension by resorting to mean-field approximation on the MDP. More specifically, we demonstrate that the formulated MDP converges to a deterministic control problem provided that the number of devices approaches infinity. We develop a low-complexity power allocation scheme to efficiently solve the original high-dimensional state space stochastic optimization problem. Our proposed power allocation policy is drawn from the solution of the Hamilton–Jacobi–Bellman equation. It can be understood as a threshold-based policy and can be implemented in a decentralized manner that makes it very appealing for high scale networks. Finally, the simulations analyses are provided to establish the effectiveness of our proposed framework.