Minimizing Uplink Delay in Delay-Sensitive 5G CRAN platforms

TL;DR

This paper proposes an optimal resource allocation method using Pareto optimization and MDP to minimize uplink delays in 5G CRAN networks, considering power control and variable service times, leading to improved delay performance.

Contribution

It introduces a novel approach combining Pareto optimization and MDP for delay minimization in 5G CRAN, addressing power control and random service times.

Findings

Significant delay reduction in simulations.

Wider stability region for user arrivals.

Improved total delay performance with power control.

Abstract

In this paper, we consider the problem of minimizing the uplink delays of users in a 5G cellular network. Such cellular network is based on a Cloud Radio Access Network (CRAN) architecture with limited fronthaul capacity, where our goal is to minimize delays of all users through an optimal resource allocation. Earlier works minimize average delay of each user assuming same transmit power for all users. Combining Pareto optimization and Markov Decision Process (MDP), we show that every desired balance in the trade-off among infinite-horizon average-reward delays, is achievable by minimizing a properly weighted sum delays. In addition, we solve the problem in two realistic scenarios; considering both power control and different (random) service times for the users. In the latter scenario, we are able to define and minimize the more preferred criterion of total delay vs. average delay for each user. We will show that the resulting problem is equivalent to a discounted-reward infinite-horizon MDP. Simulations show significant improvement in terms of wider stability region for arrival rates in power-controlled scenario and considerably reduced sum of users total delays in the case of random service times.