Joint Optimization of Preamble Selection and Access Barring for Random Access in MTC with General Device Activities

TL;DR

This paper proposes a joint optimization framework for preamble selection and access barring in MTC random access, considering arbitrary device activity distributions to improve access efficiency.

Contribution

It models device activities with multivariate Bernoulli distributions and develops algorithms for different knowledge scenarios of device activity distributions.

Findings

Outperforms existing solutions for dependent and independent device activities.

Develops algorithms for perfect, imperfect, and unknown device activity distributions.

Provides iterative algorithms with convergence guarantees.

Abstract

Most existing random access schemes for machine-type communications (MTC) simply adopt a uniform preamble selection distribution, irrespective of the underlying device activity distributions. Hence, they may yield unsatisfactory access efficiency. In this paper, we model device activities for MTC as multiple Bernoulli random variables following an arbitrary multivariate Bernoulli distribution which can reflect both dependent and independent device activities. Then, we optimize preamble selection and access barring for random access in MTC according to the underlying joint device activity distribution. Specifically, we investigate three cases of the joint device activity distribution, i.e., the cases of perfect, imperfect, and unknown joint device activity distributions, and formulate the average, worst-case average, and sample average throughput maximization problems, respectively. The problems in the three cases are challenging nonconvex problems. In the case of perfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain stationary points of the original problem and an approximate problem, respectively. In the case of imperfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain a Karush-Kuhn-Tucker (KKT) point of an equivalent problem and a stationary point of an approximate problem, respectively. Finally, in the case of unknown joint device activity distribution, we develop an iterative algorithm to obtain a stationary point. The proposed solutions are widely applicable and outperform existing solutions for dependent and independent device activities.