low complexity user selection algorithms for multiuser mimo systems with block diagonalization...
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Low Complexity User Selection Algorithms for Multiuser MIMO Systems with
Block Diagonalization
Zukang Shen, Runhua Chen, Jeff Andrews,
Robert Heath, and Brian Evans
The University of Texas at Austin
Nov. 1, 2005
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Multi-Antenna Systems Exploit the spatial dimension with multiple antennas Improve transmission reliability – diversity
Combat channel fading [Jakes, 1974]
Combat co-channel interference [Winters, 1984]
Increase spectral efficiency – multiplexing Multiple parallel spatial channels created with multiple antennas at
the transmitter and receiver [Winters, 1987] [Foschini et al., 1998] Theoretical results on point-to-point MIMO channel capacity
[Telatar, 1999]
Tradeoff between diversity and multiplexing A theoretical treatment [zheng et al., 2003]
Switching between diversity and multiplexing [Heath et al. 2005]
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Point-to-Point MIMO SystemsNarrowband system modelMIMO channel matrix
Rayleigh model, i.i.d. complex GaussianRay-tracing models [Yu et al., 2002]
Space-Time
Transmitter
Space-Time
Receiver
User Data User Data
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Downlink Multiuser MIMO SystemsDownlink: a centralized basetation communicates to
multiple users simultaneouslyBoth the basestation and users are equipped with
multiple antennasQuestions: how to utilize the spatial dimension? What
is the capacity limit?
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Capacity of MIMO Gaussian Broadcast Channels
Duality between MIMO Gaussian broadcast and multiple access channels [Vishwanath et al., 2003] [Viswanath et al., 2003]
Dirty paper coding [Costa 1983]
Sum capacity achieved with DPC [Vishwanath et al., 2003]
Iterative water-filling [Yu et al., 2004] [Jindal et al., 2005]
Capacity region of MIMO Gaussian broadcast channels [Weingarten et al., 2004]
Practical coding schemes approaching the DPC sum capacity [Zamir et al., 2002] [Airy et al., 2004] [Stojnic et al., 2004]
Too complicated for cost-effective implementations
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Block DiagonalizationBD is a Linear precoding technique
BD enforces zero inter-user interference [Spence et al., 2004] [Choi et al., 2004] [Wong et al., 2003] [Pan et al., 2004]
Effective point-to-point MIMO system
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Number of Simultaneously Supportable Users with BD
AssumptionsNumber of transmit antennasNumber of receive antennasActive users utilize all receive antennasUser channel information is known at Tx
Zero inter-user interference requires in the null space of
Dimension of :Maximum # of simultaneous users:
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The Need for Low Complexity User Selection Algorithms
Select a subset of users to maximize the total throughput when
Exhaustive searchOptimal for total throughputComputationally prohibitive
Two suboptimal user selection algorithms Linear complexity in the number of usersTotal throughput close to the optimal
Related workSemi-orthogonal user set construction [Yoo et al., 2005]
Antenna selection [Gharavi-Alkhansari et al., 2004]
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Greedy User Selection Algorithms
, apply BD to calculate the total channel energy
Apply the C-algorithm to
users selected
YesNo
Capacity Based(C-algorithm)
Channel FrobeniusNorm Based(N-algorithm)
, apply BD to calculate the sum capacity
users selected or sum capacity decreases
Yes No
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Computational Complexity
Critical matrix operations Frobenius norm Gram-Schmidt
orthogonalization Water-filling algorithm Singular value
decomposition
Proposed algorithms have complexity
Average CPU run time(Pentium M 1.6G Hz PC)