Cell-Free Massive MIMO for Future Wireless Networks
Lead Research Organisation:
University of Surrey
Department Name: Institute of Communications Systems
Abstract
To support a wide range of envisioned applications, including autonomous vehicles, the Internet of Things, and immersive technologies, future wireless technologies must meet demanding requirements for higher spectral and energy efficiency, lower end-to-end latency and massive connectivity as well as uniform user experience. Given the constraints on the available resources, the current inflexible, network-centric cellular networks cannot fulfil those disparate performance objectives. Even in massive MIMO cellular systems there remains an inherent bottleneck for cell-edge users.
The recently emerged cell-free massive MIMO (CF-mMIMO) concept is a game changer and represents a paradigm shift in the network architecture. In CF-mMIMO, cell boundaries are removed by distributing a large number of centrally connected access points over a wide service area: the resulting global network becomes fully user-centric and flexible. This provides important potential benefits, including: 1) high quality uniform user experience due to the user-centric approach; 2) superior spectral and energy efficiency through joint processing; 3) reduced latency due to distributed processing; 4) exceptional coverage probability due to distributed access points.
However, there are key challenges that need to be addressed to realise these underlying benefits of CF-MaMIMO. These challenges constitute major obstacles to keep CFmMIMO from being practically deployable. This research project aims to address those fundamental challenges. The proposed techniques will transform CF-MaMIMO into a fully user-centric, scalable, flexible system to embrace emerging AI-empowered technologies. This will enable CF-MaMIMO to support the wide range of envisioned applications in 6G and beyond. This project will benefit from strong industrial support from BT and ZTE as well as from world-leading international advisory group.
The recently emerged cell-free massive MIMO (CF-mMIMO) concept is a game changer and represents a paradigm shift in the network architecture. In CF-mMIMO, cell boundaries are removed by distributing a large number of centrally connected access points over a wide service area: the resulting global network becomes fully user-centric and flexible. This provides important potential benefits, including: 1) high quality uniform user experience due to the user-centric approach; 2) superior spectral and energy efficiency through joint processing; 3) reduced latency due to distributed processing; 4) exceptional coverage probability due to distributed access points.
However, there are key challenges that need to be addressed to realise these underlying benefits of CF-MaMIMO. These challenges constitute major obstacles to keep CFmMIMO from being practically deployable. This research project aims to address those fundamental challenges. The proposed techniques will transform CF-MaMIMO into a fully user-centric, scalable, flexible system to embrace emerging AI-empowered technologies. This will enable CF-MaMIMO to support the wide range of envisioned applications in 6G and beyond. This project will benefit from strong industrial support from BT and ZTE as well as from world-leading international advisory group.
People |
ORCID iD |
Pei Xiao (Principal Investigator) | |
Chuan Heng Foh (Co-Investigator) |
Publications
Chu Z
(2023)
Multi-IRS Assisted Multi-Cluster Wireless Powered IoT Networks
in IEEE Transactions on Wireless Communications
Chu Z
(2023)
IRS-Assisted Wireless Powered IoT Network With Multiple Resource Blocks
in IEEE Transactions on Communications
Du W
(2023)
STAR-RIS Assisted Wireless Powered IoT Networks
in IEEE Transactions on Vehicular Technology
Du W
(2023)
Hybrid Beamforming Design for ITS-Assisted Wireless Networks
in IEEE Wireless Communications Letters
Goudarzi S
(2024)
Sustainable Edge Node Computing Deployments in Distributed Manufacturing Systems
in IEEE Transactions on Consumer Electronics
Luo Q
(2023)
A Design of Low-Projection SCMA Codebooks for Ultra-Low Decoding Complexity in Downlink IoT Networks
in IEEE Transactions on Wireless Communications
Rahmani M
(2023)
Deep Reinforcement Learning-Based Sum Rate Fairness Trade-Off for Cell-Free mMIMO
in IEEE Transactions on Vehicular Technology
Yu L
(2023)
Uniform-Distributed Constellation Codebook Design for High-Capacity Visible Light Communications
in IEEE Communications Letters
Zhang L
(2023)
Energy-Efficient Task Offloading in RIS-Aided HetNets With Wireless Backhaul
in IEEE Communications Letters