Joint Optical and Wireless Resource Allocation for Cooperative Transmission in C-RAN | JoVE Visualize

Joint Optical and Wireless Resource Allocation for Cooperative Transmission in C-RAN

Peng Yang^1,2,3,4, Liao Chen^1,2,3, Hong Zhang^1,2,3

¹School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Sensors (Basel, Switzerland)|January 5, 2021

View abstract on PubMed

Summary

Cooperative multipoint transmission (CoMP) in cloud radio access networks (C-RAN) can improve signal quality but increases fronthaul load. This study introduces a resource allocation scheme to maximize cooperation gains while reducing fronthaul traffic.

Area of Science:

Wireless communication networks
Optical networking
Resource allocation algorithms

Background:

Cooperative multipoint transmission (CoMP) is crucial for mitigating interference in cloud radio access networks (C-RAN).
CoMP enhances signal quality by enabling multiple remote radio units (RRUs) to transmit data to a single user.
However, CoMP can lead to redundant data transmission, increasing the fronthaul network load in C-RAN.

Purpose of the Study:

To address the challenge of coordinating heterogeneous resource allocation in CoMP-enabled C-RAN.
To maximize cooperation gains while simultaneously reducing the fronthaul load.
To enhance fronthaul efficiency in C-RAN by integrating time and wavelength division multiplexing passive optical network (TWDM-PON).

Main Methods:

A novel multi-dimensional resource schedule (MRSCT) scheme is proposed, integrating user association, spectrum, and wavelength resource allocation.
A two-timescale resource allocation framework is established, comprising two sub-approaches.
Reinforcement learning is employed for wavelength resource allocation to alleviate fronthaul traffic, while an overlapping coalition formation game is used for dynamic spectrum allocation to reduce interference.

Main Results:

The proposed MRSCT scheme effectively maximizes fronthaul efficiency in C-RAN.
Simulation results demonstrate significant improvements in user experience and system service capability.
The integrated approach successfully balances cooperation gains with reduced fronthaul load.

Conclusions:

The MRSCT scheme offers a viable solution for optimizing resource allocation in CoMP-enabled C-RAN with TWDM-PON.
The study validates the effectiveness of combining reinforcement learning and coalition formation games for dynamic resource management.
The findings highlight the potential of MRSCT to enhance overall network performance and user satisfaction.