power efficient mimo techniques for 3gpp lte and beyond
DESCRIPTION
Power Efficient MIMO Techniques for 3GPP LTE and Beyond. K. C. Beh, C. Han, M. Nicolaou, S. Armour, A. Doufexi. Green Radio. 4 billion mobile phone users worldwide Telecommunication industry responsible for 183 million tons of CO2 - PowerPoint PPT PresentationTRANSCRIPT
Centre for Communications Research
Power Efficient MIMO Techniques for 3GPP LTE and BeyondK. C. Beh, C. Han, M. Nicolaou, S. Armour, A. Doufexi
Green Radio
• 4 billion mobile phone users worldwide
• Telecommunication industry responsible for 183 million tons of CO2
• MVCE framework (Core 5): Deliver high data rate services with a 100-fold reduction in power consumption
Green Radio and LTE
• LTE next major step in mobile radio communications
• Aim to reduce delays, improve spectrum flexibility, reduce cost of operators and end users
• MIMO transmission techniques improve system reliability and performance
• LTE support of a MIMO scheduling and precoding method with
improved interface between PHY and DLC
Green Radio and LTE
• Examine performance of proposed MIMO-OFDMA scheme
• Consider the capabilities of MIMO-OFDMA precoding in reducing Tx. Power from Base Station (BS)
• Theoretical analysis and simulation results
• Maintain QoS levels with reduced Tx. Power
System and Channel Model• Spatial Channel Model Extension (SCME) Urban Macro
• Low spatially correlated channel for all users
• 2x2 MIMO architecture (analysis is readily extendible to higher MIMO orders)
• Perfect CQI estimation and feedback
• Ideal Link Adaptation based on 6 Modulation and Coding Schemes (MCS)
System and Channel ModelTransmission Bandwidth 10 MHz
Time Slot/Sub-frame duration 0.5ms/1ms
Sub-carrier spacing 15kHz
Sampling frequency 15.36MHz (4x3.84MHz)
FFT size 1024
Number of occupiedsub-carriers
600
Number of OFDM symbols per time slot (Short/Long CP)
7/6
CP length (μs/samples)
Short (4.69/72)x6(5.21/80)x1
Long (16.67/256)
System and Channel ModelMode Modulation Cod. Rate Data bits per time slot
(1x1), (2x2)Bit Rate(Mbps)
1 QPSK 1/2 4000/7600 8/15.2
2 QPSK 3/4 6000/11400 12/22.8
3 16 QAM 1/2 8000/15200 16/30.4
4 16 QAM 3/4 12000/22800 24/45.6
5 64 QAM 1/2 12000/22800 24/45.6
6 64 QAM 3/4 18000/34200 36/68.4
Random and Layered Random Beamforming
• Random Unitary Matrix applied to frequency sub-carriers on Physical Resource Block (PRB) basis
• Linear MMSE Receiver with interference suppression capability• MIMO channels can be decomposed into separate spatial layers• ESINR feedback for resource allocation• Random Beamforming: All spatial layers to a single user• Layered Random Beamforming: Spatial layers assigned to different
users Higher Diversity
Unitary Codebook Based Beamforming
• Pre-defined set of antenna beams• Pre-coders based on Fourier basis for uniform sector coverage• Variable codebook size G, consisting of the unitary matrix set• Large Codebook: Higher Spatial Granularity, Increased Feedback• Small Codebook: Low Spatial Granularity, Lower Feedback• Single-User MIMO (SU-MIMO) and Multi-User MIMO (MU-MIMO)
capability
Feedback Considerations• Full Feedback: CQI for all
precoding matrices• Partial Feedback: CQI on
preferred beams• Suboptimal performance for MU-
MIMO with partial feedback• Codebook size G=2 assumed
Theoretical Analysis• Precoding schemes achieve varying degrees of Multiuser
Diversity (MUD) (K=5)• A target spectral efficiency achieved at different SNR levels for
different schemes
-4 -2 0 2 4 6 8 10 12 14 160
2
4
6
8
10
12
SNR (dB)
Spe
ctra
l Effi
cien
cy (
bps/
Hz)
SISOSFBC
Random Beamforming
Layered Random Beamforming
SU-MIMO
Full Feedb. MU-MIMOPartial Feedb. MU-MIMO
Theoretical Analysis• Target Spectral Efficiency 3bps/Hz• Single User SISO Benchmark• Higher benefits for increasing numbers of users• K=10, MU-MIMO, Gain= 5dB
1 5 10 15 20 250
2
4
6
8
10
12
No.of User
Per
form
ance
Gai
n O
ver
Sin
gle
Use
r S
ISO
(dB
)
SISOSFBC
Random Beamforming
Layered Random Beamforming
SU-MIMO
Full Feedb. MU-MIMOPartial Feedb. MU-MIMO
Simulation Results
• Analysis based on ideal Adaptive Modulation and Coding (AMC)
• Throughput = R(1-PER),• Results consistent with
theoretical analysis
-10 -5 0 5 10 15 20 250
1
2
3
4
5
6
7
SNR (dB)
Spe
ctra
l Eff
icie
ncy
(bps
/Hz)
SISOSFBC
Random Beamforming
Layered Random Beamforming
SU-MIMO
Full Feedb. MU-MIMOPartial Feedb. MU-MIMO
Simulation Results• Simulation performance predicts
even higher gains • Actual performance PER
dependent. • MU-MIMO and LRB eliminate deep
fades that cause severe link degradations
• MU-MIMO gain @ K=10: 7dB • SFBC suffers from inherent inability
to exploit MUD
1 5 10 15 20 250
5
10
15
No.of UserP
erfo
rman
ce G
ain
Ove
r S
ingl
e U
ser
SIS
O (
dB)
SISOSFBC
Random Beamforming
Layered Random Beamforming
SU-MIMO
Full Feedb. MU-MIMOPartial Feedb. MU-MIMO
Power Efficiency and Fairness• Power Efficiency associated with a cost metric
and a corresponding Power Fairness Index (PFI)
• Low cost metric implies high power efficiency
2
1
2
1
K
kk
kK
lk
k
R
PK
R
PPFI
K
kkk RP
1
Cost Metric Variance
SISO 2.1593 0.6923
Random Beamforming 0.9171 0.0573
Layered Random Beamforming 0.8699 0.0214
SU-MIMO 0.9172 0.0575
Full Feedb. MU-MIMO 0.8536 0.0285
Partial Feedb. MU-MIMO 0.8602 0.0312
Power Efficiency and Fairness
• PFI indication of how fairly power is allocated to different users with respect to their achieved rates
• Uplink improvements• Schemes utilising the additional
spatial layer, achieve an overall higher power allocation fairness, with PFI values consistently closer to unity.
Conclusions and Future Work• Multiuser Diversity schemes exploiting temporal, spectral and
spatial domain achieve notable performance gains. • Performance gains can be translated to a power saving at the BS• Theoretical Analysis consistent with simulation results• Improved consistency in cost metric• Improved power allocation fairness• Power savings of up to 10dB can be achieved with no QoS
compromise