Title
SIEVE: Scalable user grouping for large MU-MIMO systems
Abstract
Multi-user multiple input and multiple output (MU-MIMO) is one predominate approach to improve the wireless capacity. However, since the aggregate capacity of MU-MIMO heavily depends on the channel correlations among the mobile users in a beamforming group, unwisely selecting beamforming groups may result in reduced overall capacity, instead of increasing it. How to select users into a beamforming group becomes the bottleneck of realizing the MU-MIMO gain. The fundamental challenge for user selection is the large searching space, and hence there exists a tradeoff between search complexity and achievable capacity. Previous works have proposed several low complexity heuristic algorithms, but they suffer a significant capacity loss. In this paper, we present a novel MU-MIMO MAC, called SIEVE. The core of SIEVE design is its scalable multi-user selection module that provides a knob to control the aggressiveness in searching the best beamforming group. SIEVE maintains a central database to track the channel and the coherence time for each mobile user, and largely avoids unnecessary computing with a progressive update strategy. Our evaluation, via both small-scale testbed experiments and large-scale trace-driven simulations, shows that SIEVE can achieve around 90% of the capacity compared to exhaustive search.
Year
DOI
Venue
2015
10.1109/INFOCOM.2015.7218581
IEEE International Conference on Computer Communications
Field
DocType
ISSN
Beamforming,Bottleneck,Multi-user MIMO,Capacity loss,Brute-force search,Computer science,MIMO,Computer network,Sieve,Distributed computing,Scalability
Conference
0743-166X
Citations 
PageRank 
References 
11
0.60
19
Authors
4
Name
Order
Citations
PageRank
Wei-Liang Shen1594.44
Kate Ching-Ju Lin241838.28
Ming Chen365071277.71
Kun Tan4135098.64