Cross-Layer Optimization for Statistical QoS Provision in C-RAN with Finite-Length Coding

TL;DR

This paper introduces a cross-layer optimization framework for C-RAN that maximizes bandwidth efficiency while ensuring statistical QoS, using finite-length coding and joint decision-making across multiple network layers.

Contribution

It proposes a novel joint optimization model for C-RAN that integrates physical, data link, and transport layer parameters considering finite-length coding effects.

Findings

Bandwidth efficiency improved by up to 27.87% with the proposed algorithms.

The model accurately captures QoS and channel quality impacts on transmission parameters.

Decomposition into TPD and UA sub-problems enables efficient solution methods.

Abstract

The cloud radio access network (C-RAN) has become the foundational structure for various emerging communication paradigms, leveraging the flexible deployment of distributed access points (APs) and centralized task processing. In this paper, we propose a cross-layer optimization framework based on a practical finite-length coding communication system in C-RAN, aiming at maximizing bandwidth efficiency while providing statistical quality of service (QoS) for individual services. Based on the theoretical results from effective capacity and finite-length coding, we formulate a joint optimization problem involving modulation and coding schemes (MCS), retransmission count, initial bandwidth allocation and AP selection, which reflects the coordinated decision of parameters across the physical layer, data link layer and transport layer. To tackle such a mixed-integer nonlinear programming (MINLP) problem, we firstly decompose it into a transmission parameter decision (TPD) sub-problem and a user association (UA) sub-problem, which can be solved by a binary search-based algorithm and an auction-based algorithm respectively. Simulation results demonstrate that the proposed model can accurately capture the impact of QoS requirements and channel quality on the optimal transmission parameters. Furthermore, compared with fixed transmission parameter setting, the proposed algorithms achieve the bandwidth efficiency gain up to 27.87% under various traffic and channel scenarios.