Game-Theoretic Optimization for Machine-Type Communications Under QoS Guarantee

TL;DR

This paper introduces a game-theoretic approach to optimize access control and data transmission in massive machine-type communication networks, ensuring quality of service guarantees under strict delay constraints.

Contribution

It develops a novel priority-queueing-based ACB model, formulates it as a non-cooperative game, and proposes algorithms with proven convergence to optimize network performance.

Findings

Effective capacity is accurately modeled for mMTC.

The Nash equilibrium exists and is unique.

Proposed algorithms converge and improve network efficiency.

Abstract

Massive machine-type communication (mMTC) is a new focus of services in fifth generation (5G) communication networks. The associated stringent delay requirement of end-to-end (E2E) service deliveries poses technical challenges. In this paper, we propose a joint random access and data transmission protocol for mMTC to guarantee E2E service quality of different traffic types. First, we develop a priority-queueing-based access class barring (ACB) model and a novel effective capacity is derived. Then, we model the priority-queueing-based ACB policy as a non-cooperative game, where utility is defined as the difference between effective capacity and access penalty price. We prove the existence and uniqueness of Nash equilibrium (NE) of the non-cooperative game, which is also a sub-modular utility maximization problem and can be solved by a greedy updating algorithm with convergence to the unique NE. To further improve the efficiency, we present a price-update algorithm, which converges to a local optimum. Simulations demonstrate the performance of the derived effective capacity and the effectiveness of the proposed algorithms.