Multu user diversity in slow fading channel

1. sb344@njit.edu 1
Multi-user diversity in
slow fading channels
Reference:
“Opportunistic Beamforming Using Dumb Antennas”
P. Vishwanath, D. Tse, R. Laroia, IT 2002
Presented by:
Sarandeep Bhatia

2. sb344@njit.edu 2
ReviewReview
 Fading : Rapid fluctuations of signal strength due to
constructive and destructive interference between multi-
paths.
 Diversity : Technique to compensate for fading channel
impairments. It can be obtained over:
Time - Interleaving of coded bits
Frequency – Spread spectrum & frequency hopping
Space – Multiple antennas
Fast fading channels : Diversity inherent
Slow fading channels: Diversity induced

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Focus on downlink of wirelessFocus on downlink of wireless
communicationcommunication
Multiple antennas at the base station to transmit the same
signal.
Fundamental difference :
“Multi-user diversity takes advantage of rather than
Compensate fading”

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Opportunistic Beam formingOpportunistic Beam forming
The information bearing signal at each of the transmit antenna
is multiplied by a random complex gain.
Formation of random beam.

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Slow Fading Environment : BeforeSlow Fading Environment : Before

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Slow Fading Environment : AfterSlow Fading Environment : After

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Fading channel is Better Than AWGNFading channel is Better Than AWGN
Total average SNR = 0 dB.
Long term total throughput can be maximized by always
serving the user with the strongest channel.

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Maximizing information theoretic capacityMaximizing information theoretic capacity
 Strategy –
--In a large system with users channels fading
independently, there is likely to be a user with a very
good channel at any time.
--Schedule to the user with best channel to transmit to
base station.
 Assumption –
--Channel tracked by receiver and SNR fed back to BS.
--Peak transmit power constraint.

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Issues in schedulingIssues in scheduling
 Fading statistics identical:
-- Strategy not only maximizes the total capacity but
also throughput of individual users.
 Fading statistics different : Two major issues
-- Fairness
-- Delay

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Proportional Fair SchedulingProportional Fair Scheduling
At time slot t, given
 User’s average throughputs T1(t), T2(t)…in past window of
time tc
 Feedback of channel quality in terms of requested data rate
R1(t), R2(t)…
 Schedule the user ‘k’ with the highest ratio
Rk = current requested rate of user k
Tk = average throughput of user k in the past tc time slots.
Average throughputs Tk (t) updated by an exponential filter.

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Inspection of algorithmInspection of algorithm
 When tc is small- Serves all users
 When tc is large
-- Case-1 : Identical channels
Tk remains same .Pick user with greater Rk.
-- Case-2 : Different channels
If Tk is large then Rk is also large. Pick user with
greater

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Comparison with space time codeComparison with space time code
 Space time code : Intelligent use of transmit diversity to
improve reliability of point-to-point link but reduce multi-
user diversity gain.
 In contrast, opportunistic beam forming requires no
special multi-antenna encoder or decoder nor MIMO
channel estimation.
 Use of separate pilot signals for each antenna in space
time codes.
 Antennas are truly dumb, but yet can surpass
performance of space time code (with proportional
scheduling).

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Overall performance ImprovementOverall performance Improvement

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Cellular Environment : Opportunistic NullingCellular Environment : Opportunistic Nulling
 In a cellular systems, users are scheduled when their
channel is strong and interference from adjacent base
station is weak.
 Multi-user diversity allows interference avoidance as there
is beamforming to some users and null to other users.
 Opportunistic beamforming combined with opportunistic
nulling.

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ConclusionConclusion
 Modern design principle :
“Large and Rapid channel fluctuations are
preferable”
 Proactive Stance :
“Induce Larger and Faster Channel fluctuations”
 Requirement :
-- Sufficient number of users in the system
-- Scheduling algorithm

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Questions????Questions????