Abstract | ||
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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 |
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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 Shen | 1 | 59 | 4.44 |
Kate Ching-Ju Lin | 2 | 418 | 38.28 |
Ming Chen | 3 | 6507 | 1277.71 |
Kun Tan | 4 | 1350 | 98.64 |