The University of Southampton
University of Southampton Institutional Repository

Dynamic user-centric clustering for uplink cooperation in multi-cell wireless networks

Dynamic user-centric clustering for uplink cooperation in multi-cell wireless networks
Dynamic user-centric clustering for uplink cooperation in multi-cell wireless networks
In this work, dynamic user-centric cell clustering, which is capable of exploiting dynamics in the channel states, is investigated for joint intra-cluster interference cancellation in a multi-cell environment. A resource efficient cluster size minimization problem is formulated to dynamically group the cells into clusters based on user channel states such that quality-of-service (QoS) provisioning for cell-edge users can be improved. The integer programming problem depends largely on the intra-cluster interference weight. A subgradient algorithm is employed to solve the relaxed problem when no constrain on cooperation cost is present. To reduce extra burden on backhaul due to base station (BS) cooperation, constraints on the number of per-BS cooperative links and the maximum user-centric cluster size are introduced to the optimization problem, which is solved efficiently by a greedy algorithm. Numerical results show that the proposed dynamic user-centric clustering algorithms achieve significant improvements over existing static and fixed-size dynamic clustering schemes in terms of cell-edge performance and backhaul efficiency. The proposed greedy algorithm, in particular, can effectively alleviate the overall and per-BS cooperation cost while guaranteeing the cooperative gain. With similar resource consumption and outage performance, the proposed scheme achieves 12.6% higher rate gain compared with existing fixed size dynamic clustering strategy.
2169-3536
1-14
Zhang, Zhe
64eac7e3-a8d2-4243-a450-03ce0888233c
Wang, Ning
bdc70dc7-116c-4868-b5b3-97f00bfcd480
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Mu, Xiaomin
a069856c-d524-4908-a47a-6aff35a0cf26
Zhang, Zhe
64eac7e3-a8d2-4243-a450-03ce0888233c
Wang, Ning
bdc70dc7-116c-4868-b5b3-97f00bfcd480
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Mu, Xiaomin
a069856c-d524-4908-a47a-6aff35a0cf26

Zhang, Zhe, Wang, Ning, Zhang, Jiankang and Mu, Xiaomin (2018) Dynamic user-centric clustering for uplink cooperation in multi-cell wireless networks. IEEE Access, 1-14. (doi:10.1109/ACCESS.2018.2792222).

Record type: Article

Abstract

In this work, dynamic user-centric cell clustering, which is capable of exploiting dynamics in the channel states, is investigated for joint intra-cluster interference cancellation in a multi-cell environment. A resource efficient cluster size minimization problem is formulated to dynamically group the cells into clusters based on user channel states such that quality-of-service (QoS) provisioning for cell-edge users can be improved. The integer programming problem depends largely on the intra-cluster interference weight. A subgradient algorithm is employed to solve the relaxed problem when no constrain on cooperation cost is present. To reduce extra burden on backhaul due to base station (BS) cooperation, constraints on the number of per-BS cooperative links and the maximum user-centric cluster size are introduced to the optimization problem, which is solved efficiently by a greedy algorithm. Numerical results show that the proposed dynamic user-centric clustering algorithms achieve significant improvements over existing static and fixed-size dynamic clustering schemes in terms of cell-edge performance and backhaul efficiency. The proposed greedy algorithm, in particular, can effectively alleviate the overall and per-BS cooperation cost while guaranteeing the cooperative gain. With similar resource consumption and outage performance, the proposed scheme achieves 12.6% higher rate gain compared with existing fixed size dynamic clustering strategy.

Text
08260866 - Version of Record
Available under License Creative Commons Attribution.
Download (1MB)

More information

Accepted/In Press date: 22 December 2017
e-pub ahead of print date: 17 January 2018

Identifiers

Local EPrints ID: 417260
URI: http://eprints.soton.ac.uk/id/eprint/417260
ISSN: 2169-3536
PURE UUID: 47cb06f4-ab5f-4d52-b23d-dfecaaf2c2ab
ORCID for Jiankang Zhang: ORCID iD orcid.org/0000-0001-5316-1711

Catalogue record

Date deposited: 26 Jan 2018 17:30
Last modified: 07 Oct 2020 04:15

Export record

Altmetrics

Contributors

Author: Zhe Zhang
Author: Ning Wang
Author: Jiankang Zhang ORCID iD
Author: Xiaomin Mu

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×