Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Topology control for effective interference cancellation in multiuser MIMO networks

Gelal, E and Ning, J and Pelechrinis, K and Kim, TS and Broustis, I and Krishnamurthy, SV and Rao, BD (2013) Topology control for effective interference cancellation in multiuser MIMO networks. IEEE/ACM Transactions on Networking, 21 (2). 455 - 468. ISSN 1063-6692

[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)

Abstract

In multiuser multiple-input-multiple-output (MIMO) networks, receivers decode multiple concurrent signals using successive interference cancellation (SIC). With SIC, a weak target signal can be deciphered in the presence of stronger interfering signals. However, this is only feasible if each strong interfering signal satisfies a signal-to-noise-plus-interference ratio (SINR) requirement. This necessitates the appropriate selection of a subset of links that can be concurrently active in each receiver's neighborhood; in other words, a subtopology consisting of links that can be simultaneously active in the network is to be formed. If the selected subtopologies are of small size, the delay between the transmission opportunities on a link increases. Thus, care should be taken to form a limited number of subtopologies. We find that the problem of constructing the minimum number of subtopologies such that SIC decoding is successful with a desired probability threshold is NP-hard. Given this, we propose MUSIC, a framework that greedily forms and activates subtopologies in a way that favors successful SIC decoding with a high probability. MUSIC also ensures that the number of selected subtopologies is kept small. We provide both a centralized and a distributed version of our framework. We prove that our centralized version approximates the optimal solution for the considered problem. We also perform extensive simulations to demonstrate that: 1) MUSIC forms a small number of subtopologies that enable efficient SIC operations; the number of subtopologies formed is at most 17% larger than the optimum number of topologies, discovered through exhaustive search (in small networks); 2) MUSIC outperforms approaches that simply consider the number of antennas as a measure for determining the links that can be simultaneously active. Specifically, MUSIC provides throughput improvements of up to four times, as compared to such an approach, in various topological settings. The improvements can be directly attributable to a significantly higher probability of correct SIC based decoding with MUSIC. © 1993-2012 IEEE.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Gelal, E
Ning, J
Pelechrinis, Kkpele@pitt.eduKPELE
Kim, TS
Broustis, I
Krishnamurthy, SV
Rao, BD
Date: 22 April 2013
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: IEEE/ACM Transactions on Networking
Volume: 21
Number: 2
Page Range: 455 - 468
DOI or Unique Handle: 10.1109/tnet.2012.2205160
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Telecommunications
Refereed: Yes
ISSN: 1063-6692
Date Deposited: 13 Jun 2012 15:37
Last Modified: 02 Feb 2019 16:55
URI: http://d-scholarship.pitt.edu/id/eprint/12372

Metrics

Monthly Views for the past 3 years

Plum Analytics

Altmetric.com


Actions (login required)

View Item View Item