The document discusses spatial interference and its effect on future 4G wireless networks. It provides background on the problem of spatial interference in multiple-input multiple-output (MIMO) systems which can degrade capacity. Previous works that attempted to address this issue through techniques like antenna separation, optimum power allocation, and subcarrier allocation are summarized. The proposed solution will look to further mitigate the effects of spatial interference in orthogonal frequency-division multiple access (OFDMA) systems.

1. Monthly Faculty Seminar
Spatial Interference and Its Effect
Towards the Implementation of
Future 4G Wireless Networks
Dr. Rosdiadee Nordin
JKEES, FKAB

2. LAYOUT
• Introduction
• Problem Background
• Previous Works
• Proposed Solution
• Results and Analysis
• Future Works
• Conclusions

3. INTRODUCTION
• Wireless technologies – Past, Present and Future
• Imperfection within the wireless channel present
numerous challenges – fading vs. Shannon‟s capacity
• Basic mechanisms for wireless propagation:
– Reflection Base Station
– Diffraction
– Scattering lect
io n
Ref
S
g
Lo
in
ter
S cat
io n
act
ffr
Di
Local
Scattering

4. INTRODUCTION
• Type of imperfections:
– Large-scale fading:
• Power varies gradually
• Over large distance, terrain contours
• Determine by path profile and antenna displacement
– Small-scale fading:
• Small changes of the reflected, diffracted and scattered
signals
• Resulting in vector summation of destructive/ constructive
interference at Rx, known as multipath wave
• Rapid changes of amplitudes, phase or angle
• Also known as Rayleigh fading [1] or frequency selectivity
[1] J.G. Proakis. Digital Communications. Fourth Edition, The McGraw-Hill Companies, 2001

5. SMALL-SCALE FADING
• Rayleigh fading induces Inter-symbol
Interference (ISI) – major source of impairment
in wireless channel
• Influenced by following mechanisms:
– Time spreading
10
5
– Time variance 0
-5
Power (dB)
-10
-15
-20
-25
-30
-35
0 20 40 60 80 100 120 140 160 180 200
time (ms)

6. MITIGATION STRATEGIES
• Introducing diversity [2]:
– Time: coding, interleaving, adaptive modulation,
equalization (linear/ non-linear)
– Spatial: multiple antenna, involves combining
methods
– Multiuser: exploit channel quality from different users
– Cooperative: relay
– Frequency: spread spectrum (DS and FH), SC-FDE
[3], OFDM [4]
[2] B. Sklar. “Rayleigh Fading Channels in Mobile Digital Communications Systems. Part II: Mitigation”, IEEE
Communications Magazine, Vol. 35, No. 7, pp. 102-109, July 1997
[3] H. Sari, G. Karam, and I. Jeanclaude. “Transmission techniques for digital terrestrial TV broadcasting”. IEEE
Communications Magazine, Vol. 33, No. 2, pp. 100-109, Feb., 1995.
[4] R. van Nee and R. Prasad. OFDM for Wireless Multimedia Communications. Artech House Publishers, 2000.

7. OFDM
• Multi-carrier modulation scheme – splitting the high rate
data stream into lower rate data streams
• From wideband into orthogonal narrow band signals
• Addition of Guard Interval (GI) provides effective way to
combat ISI
• FFT (Rx) and IFFT (Tx) help to reduce the complexity of
OFDM implementation

8. OFDM
• OFDM has several disadvantages:
– PAPR, will reduce the ADC/DAC and inefficient power
amplifier. Mitigations: clipping, reduction codes and
scrambling techniques
– Frequency offset synchronization. Mitigation: training
sequence
– Phase noise. Mitigation: pilot signal

9. OFDM(A)
• An extension to OFDM, or the multiuser version of
OFDM is known as OFDMA
• Strong candidate for future 4G air interface downlink
transmission
• Employs bigger FFT size, flexible timeslots and
subchannels size, support different type of QoS,
feedback information to enhance resource allocation.
• Subcarrier as the smallest unit in an OFDMA
transmission

10. MIMO
• Another potential technology for future 4G. Have been
around since 1970 [5]
• Leveraging multipath – exploiting fading (instead of
mitigating) for the benefit of MIMO users
• CMIMO = min(Nt,Nr). Increasing the number of Tx, Rx
antenna allows more data to be transmitted
Radio Channel, H
x1 1 1 y1 ŷ1
Tx1 Rx1
2
x2 2 y2 ŷ2
Tx2 Rx2 MIMO
Processing
x Nt Nt Nr yNr ŷNr
Tx Nt Rx Nr
[5] A.R. Kaye and D.A. George. “Transmission of multiplexed PAM signals over multiple channel and diversity systems,”
IEEE Trans. on Comms. Technology. Vol. COMM-18, pp. 520-526, Oct. 1970

11. MIMO
• Can takes many forms for different type of configurations
• SISO vs. MIMO capacity [6]:
2 SNR
C SISO log 2 (1 SNR H ) vs. C MIMO log 2 det I N HH*
Nt r
• Two forms of MIMO:
– Space-Time Code (STC)
– Spatial Multiplexing (SM)
[6] D. Gesbert, M. Shafi, D.S Shiu, P.J. Smith, and A. Naguib. “From theory to practice: an overview of MIMO space-time
coded wireless systems”, Tutorial paper. IEEE Journal on Selected Areas in Communications (JSAC), Vol. 21, No. 3, pp.
281-302, Apr. 2003

12. MIMO – STC
• STC aim to reduce the effect of fading by:
– Achieve maximum antenna diversity
– Improve wireless link reliability
• Combines the use of channel coding and multiple
transmit antennas
• Achieve spatial diversity at the expense of throughput
• Common forms of STC:
– Space-Time Trellis Code (STTC)
– Space-Time Block Code (STBC)
– Space-Frequency Block Code (SFBC)
• Practical and common representation of STBC was
introduced by Alamouti [7]
[7] S. Alamouti, “A simple transmit diversity technique for wireless communications”, IEEE Journal on Selected
Areas in Communications (JSAC), Vol. 16, No. 8, pp. 1451-1458, Oct. 1998

13. MIMO – SM
• Capable of increasing the data rate by higher spectral
efficiencies at no additional power or bandwidth
• Dividing the high rate data stream input into parallel
independent data streams.
• Thus, increased the nominal spectral efficiencies by a
factor of Nt.
• V-BLAST [8] generally regarded as the common form of
SM
Antenna Index Antenna Index
Interference
a b c d Nulled
a a a a
Wasted
a b c d
b b b b
a b c d
c c c c
Wasted
a b c d
Time d d d d
Cancelled Detection Order Time
D-BLAST V-BLAST
[8] G. J. Foschini, “Layered Space-Time Architecture for Wireless Communication in a Fading Environment when Using
Multielement Antennas,” Bell Labs Tech. J., pp. 41–59, Autumn 1996

14. MIMO – SM
• SM performance relies on the:
– Detection techniques at Rx
– Presence of (rich) scatterers
• Some of the well-known SM detection techniques:
– Successive Interference Cancellation (SIC)
– Zero Forcing (ZF)/ Interference nulling/ Linear decorrelator
– Min. Mean Square Error (MMSE)
• Scatterers: characteristics of the scatters can be
determine from the propagation profile, e.g. separation,
location, surrounding terrain, etc.

15. PROBLEM BACKGROUND
• SM relies on the linear dependence between the channel
responses corresponding to each transmit antenna.
• Suffer considerably from
– Spatial subchannels correlation and/or,
– Ricean fading component
• Result in an near rank one MIMO channel matrix.
• Cause degradation of downlink capacity. Retransmission
and combining techniques does not improve the BER
performance.

16. PROBLEM BACKGROUND
• Most MIMO implementations consider ideal propagation
conditions, i.e. uncorrelated channel
• For realistic approach, spatial correlation does exist
between antenna pairs – affects MIMO capacity
• The effect is known as self-interference [6]
12
RBS=0.0,RMS=0.0
RBS=0.4,RMS=0.4
10
Spatial layer 1 RBS=0.5,RMS=0.5
T1 R1 RBS=0.0,RMS=0.9
Spatial layer 2 8 RBS=0.9,RMS=0.0
capacity (bps/Hz)
Interference RBS=0.9,RMS=0.9
from T2 6 RBS=1.0,RMS=1.0
BS Interference MS
from T1 4
T2 Spatial layer 1 R2
Spatial layer 2
2
0
-10 -5 0 5 10 15 20
SNR (dB)
[6] D. Gesbert, M. Shafi, D.S Shiu, P.J. Smith, and A. Naguib. “From theory to practice: an overview of MIMO space-time
coded wireless systems”, Tutorial paper. IEEE Journal on Selected Areas in Communications (JSAC), Vol. 21, No. 3, pp.
281-302, Apr. 2003

17. PROBLEM BACKGROUND
• Factors contribute towards self-interference:
– Insufficient antenna separation
– Small scattering angle, e.g. AoA, AoD, etc
– Height of BS antennas
– Separation between Tx and Rx antenna
• Design antenna based on degree of correlation [9], e.g.
100 separation and wider angle (max. 900)
• Not possible due to RF planning, safety, environmental
and installation issue
several km
small small
[9] W. Lee, "Effects on Correlation between Two Mobile Radio Base-Station Antennas," IEEE Transactions on
Communications, Vol.21, No.11, pp. 1214-1224, Nov 1973

18. PREVIOUS WORKS
• Efficient design techniques for MIMO antenna
implementation
– Antenna separation
– Orthogonality: angle, space, polarization
• Challenges:
– Environmental and safety concern
– Array blindness
– Reduction of antenna effective gain
– „Keyhole‟ or „pinhole‟ effect [10]
[10] D. Chizhik, G. J. Foschini, and R.A. Valenzuela, “Capacities of multi-element transmit and receive
antennas: Correlations and Keyholes,” Electronic Letters, Vol. 36, pp. 1099–1100, June 2000

19. PREVIOUS WORKS
• Optimum power allocation scheme – known CSI at the
Tx via SVD [11]
• Singular values as the decision criteria for power
allocation – to identify effective independent channel
• Challenges:
– Inaccurate CSI in fast fading channels
– Different eigenvalues in each channel – errors in selection
criteria
– High spatial correlation  low eigenvalues  low gain (power
loss)
[11] R.R. Ramirez and F. De Flaviis, "A mutual coupling study of linear and circular polarized microstrip antennas for
diversity wireless systems", IEEE Transactions on Antennas and Propagation, Vol. 51, No. 2, pp. 238-248, Feb. 2003

20. PREVIOUS WORKS
• Enhanced version of [11]: antenna selection + power
allocation [12]
• Using the correlation matrix, instead of CSI as feedback:
less overhead, low feedback req. and faster allocation
process
• „Water-filling‟ approach
• Challenges:
• Requires continuous bit assignment
• But modulations is discrete; can be
overcome by AMC
• At the expense of data rate loss
[12] M.T. Ivrlac, W. Utschick, J.A. Nossek, "Fading correlations in wireless MIMO communication systems",
IEEE Journal on Selected Areas in Communications, Vol. 21, No. 5, pp. 819- 828, June 2003

21. PREVIOUS WORKS
• Constellation multiplexing [13]: the use of power scaling
by scaling down the desired M-QAM constellation size
• Adjust the power and phase of the input constellations
• In a 16-QAM, superposed of 2×4-QAM
s2
signals), scaled down to ¼ with BER te
xt
te
xt
te
xt
te
xt
loss of 4 dB te te te
s1
te
xt xt xt xt
• But, requires one transmit antenna to
te te te te
Tx and Rx xt xt xt xt
• Only „dual mode‟ operation te
xt
te
xt
te
xt
te
xt
[13] J. Akhtar, D. Gesbert, "A closed-form precoder for spatial multiplexing over correlated MIMO channels", IEEE
Global Telecommunications Conference, 2003. GLOBECOM '03, Vol. 4, pp. 1847-1851, Dec. 2003

22. PREVIOUS WORKS
• Subcarrier allocation scheme based on the knowledge of
the adjacent spatial sub-channels – DSA Scheme 5 [14]
• Avoid selection of (i) similar subcarrier from the adjacent
spatial subchannel, and (ii) the near subcarriers
• Depends on the separation between current and next
allocated subcarrier, j
• Depends on the channel model profile
The queue by metric of channel gain of
subcarrier at the certain subchannel A and B
21 38 89 128 328 437
Previously considered
spatial subchannel
(subchannel A)
21 26 30 71 105 128
The considered spatial
The allocated subchannel for the same
subcarrier user (subchannel B)
[14] Y. Peng, S. Armour, A. Doufexi, J. McGeehan, “An Investigation of Optimal Solution for Multiuser Sub-carrier
Allocation in OFDMA Systems”, IEEE Multi-Carrier Spread-Spectrum Workshop (MCSS): Proceedings from the 5th
International Workshop: pp. 337-344. Germany, Sep. 2005

23. PREVIOUS WORKS
• Swapping of subcarriers between users, known as
MGSS [15] to achieve max. power gain
• Total perceived gain as the performance metric
• Involved two stages:
– Initial allocations: fast & rough 0
10
version of the allocation matrix
– Sort-swap: iterative process to
Bit Error Rate (BER)
-1
10
refine the allocation
• However, MGSS has poor -2
10 Uncorr
HL
HH
performance against self- CH
Full
-3
interference 10
-5 0 5 10 15 20
Signal-to-Noise Ratio (SNR) in dB
25 30
• Modification is required
[15] S. Pietrzyk, G.J.M Janssen, “Multiuser subcarrier allocation for QoS provision in the OFDMA systems”, IEEE
56th Vehicular Technology Conference, 2002. VTC 2002-Fall, Vol.2, pp. 1077- 1081, Sept. 2002

24. PROPOSED SOLUTION
• OFDMA allows multiple users to Tx simultaneously on
different subcarriers by exploiting channel fading
• Initial work done based on SISO transmission [16]
• ESINR as performance metric, known as DSA-ESINR
• Involves sorting, comparing and simple arithmetic
• Ranks users from lowest to highest ESINR – fairness
MMSE filter
q= spatial layer Main spatial layer
2
Gk H k qq Es
q
ESINRk 2 2 2
Gk H k Es Gk Gk N
Channel Gain
qj, j q qq qj, j q
M A
FD
k= subcarrier index Knowledge of TDMA
self-interference
[16] A. Doufexi and S. Armour, "Design Considerations and Physical Layer Performance Results for a 4G OFDMA System
Employing Dynamic Subcarrier Allocation", IEEE 16th International Symposium on Personal, Indoor and Mobile Radio
Communications, 2005. PIMRC 2005, Vol. 1, pp. 357-361, Sept. 2005

25. PROPOSED SOLUTION
ESINR
s0 INR r0
ES
BS ES
INR MS ‘All’ ESINR
`
s1 r1
ESINR
ESINR
ain
lg r0
s0 nn
e
C ha
BS Ch
an MS ‘Partial’ ESINR
ne
`
l ga
in
s1 r1
ESINR

26. SIMULATION SETUPS
• Nsub= 768, NFFT= 1024 for 16 users, 48 subcarriers per
user, 2×2 MIMO configuration
• Six MCS schemes, consists of BPSK, QPSK, 16-QAM
and 64-QAM with ½ or ¾ coding rate 0.9
1
0.8
• Two channel models: 0.7
Normalised power
0.6
– ETSI HiperLAN „Channel E‟ [17]
0.5
0.4
0.3
– 3GPP-SCM „Urban Micro‟ [18] 0.2
0.1
HIPERLAN ‘E’
0 200 400 600 800 1000 1200 1400 1600 1800
Parameters Urban Micro Excess delay (ns)
1
Environment Large open space NLOS Outdoor urban NLOS 0.9
0.8
Bandwidth 100 MHz 5 MHz
0.7
Normalised power
Excess Delay Spread 1760 ns 923 ns 0.6
0.5
Mean Delay Spread 250 ns 251 ns 0.4
Carrier Frequency 5 GHz 2 GHz 0.3
0.2
0.1
[17] J. Medbo and P. Schramm, "Channel Models for HIPERLAN/2," ETSI/BRAN 200 300 400 500 600 700 800 900 1000
Excess delay (ns)
document no. 3ERI085B, 1998.
[18] 3GPP, “Spatial channel model for MIMO simulations”, TR 25.996 V7.0.0, 3GPP,
2007. [Online]. Available: http://www.3gpp.org/

27. SIMULATION SETUPS
• Correlation model based on Kronecker product,
RMIMO=RMS RBS [19]
Correlation
• „Fully‟ correlated channel: worst case
Correlation
Coefficient
Modes
RBS RMS
scenario, i.e. SISO case
„Full‟ 0.99 0.99
• Uncorrelated channel: „ideal‟ channel „CH‟ 0.96 0.96
„HH‟
condition 0.91 0.91
„HL‟ 0.91 0.30
• Issue: how uncorrelated is an Uncorrelated 0.00 0.00
uncorrelated channel? Correlation
Correlation
• Uncorrelated: „Default‟ vs „Forced‟
Coefficient
Modes
RBS RMS
– ‘Default’: Generated by the channel models „Default‟ 0.45 0.32
– ‘Forced’: No effect of self-interference „Forced‟ 0.00 0.00
[19] K.I. Pedersen, P.E. Mogensen, B.H. Fleury, “Spatial Channel Characteristics in Outdoor Environments and Their
Impact on BS antenna System Performance”, IEEE Proc. Vehicular Technology Conference. VTC ’98, Vol. 2, pp. 719-724,
May 1998.

28. SIMULATION SETUPS
ETSI’s Channel E SCM’s Urban Micro
15 10
10 5
5 0
0 -5
Transmit Power (dB)
Transmit Power (dB)
-5 -10
-10 -15
-20
-15
-25 h
-20 h 11
11
h
h -30 12
-25 12
h
h 21
21 -35
-30 h
22
h
22 -40
-35 0 100 200 300 400 500 600 700
0 100 200 300 400 500 600 700 Subcarrier
Subcarrier
Uncorrelated Channel Spatial layer 1
T1 R1
Spatial layer 2
Interference
from T2
BS Interference MS
from T1
T2 Spatial layer 1 R2
Spatial layer 2

29. SIMULATION SETUPS
ETSI’s Channel E SCM’s Urban Micro
10
15
5
10
0
5
-5
Transmit Power (dB)
0
-10
Transmit Power (dB)
-5
-15
-10
-15
-20
-20 h -25 h11
11
-25
h
12 -30 h12
h
21
h21
-30 -35
h
22
h22
-35 -40
0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700
Subcarrier Subcarrier
‘HH’ Correlated Channel

30. SIMULATION SETUPS
ETSI’s Channel E SCM’s Urban Micro
10 5
0
0
-5
Transmit Power (dB)
Transmit Power (dB)
-10 -10
-15
-20
-20
-30 h11 -25 h11
h12 -30 h12
-40 h21 h21
-35
h22 h22
-50 -40
0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700
Subcarrier Subcarrier
‘Fully’ Correlated Channel

31. RESULTS AND ANALYSIS
0
Uncorrelated channel 10
SM-Uncorr
STC-Uncorr
SM-HH
• SM vs. STBC performance STC-HH
Bit Error Rate (BER)
-1
10 SM-CH
STC-CH
• „Force‟ vs. „Default‟ SM-Full
STC-Full
• „Partial-SINR‟ vs. „All-SINR‟ 10
-2
0 -3
10 10
-10 -5 0 5 10 15 20
Partial DSA-SINR Signal-to-Noise Ratio (SNR) in dB
0
All DSA-SINR 10
DSA-Sch 1 'Force' Uncorrelated
Bit Error Rate (BER)
-1
'Default' Uncorrelated
10 AWGN Channel
Bit Error Rate (BER)
-1
10
-2
10
-2
10
-3
10
-5 0 5 10 15
Signal-to-Noise Ratio (SNR) in dB -3
10
-5 0 5 10 15
Signal-to-Noise Ratio (SNR) in dB
2 dB

32. RESULTS AND ANALYSIS
• Fairness gain
5
Channel Gain 14
4 Channel Gain
ESINR
12 ESINR
3 Random
Random
2 10
Average (dB)
1
Variance
8
0
6
-1
4
-2
-3 2
-4
2 4 6 8 10 12 14 16 0
User number 2 4 6 8 10 12 14 16
User number
(a) Average ESINR metric (b) Mean variance
Algorithm ESINR Channel gain Random
Mean (dB) 1.815 1.658 -1.336
Variance 0.6061 1.882 4.972

33. RESULTS AND ANALYSIS
0 0
10 10
DSA-ESINR M1 DSA-ESINR M1
Bit Error Rate (BER)
Bit Error Rate (BER)
-1 DSA-Sch1 M1 -1
10 10 DSA-Sch1 M1
DSA-ESINR M2
DSA-ESINR M2
DSA-Sch1 M2
DSA-Sch1 M2
DSA-ESINR M3
DSA-ESINR M3
DSA-Sch1 M3
DSA-Sch1 M3
-2 DSA-ESINR M4
10 -2 DSA-ESINR M4
DSA-Sch1 M4 10 DSA-Sch1 M4
DSA-ESINR M5
DSA-ESINR M5
DSA-Sch1 M5
DSA-Sch1 M5
DSA-ESINR M6
DSA-ESINR M6
-3 DSA-Sch1 M6
10 -3 DSA-Sch1 M6
-20 -10 0 10 20 30 10
Signal-to-Noise Ratio (SNR) in dB -20 -10 0 10 20 30
Signal-to-Noise Ratio (SNR) in dB
„Forced‟ „Default‟

34. RESULTS AND ANALYSIS
• Correlated channel – comparison between two different
allocation schemes in a QPSK, ½ MCS SM-OFDMA
downlink (16 users)
0
10 0
10
Uncorr
HL
HH
Bit Error Rate (BER)
-1 CH
Bit Error Rate (BER)
10 -1
10 Full
-2
10 Uncorr -2
10
HL
HH
CH
-3
Full
10 -3
-10 0 10 20 30 10
Signal-to-Noise Ratio (SNR) in dB -10 -5 0 5 10
Signal-to-Noise Ratio (SNR) in dB
(a) Channel gain (b) ESINR

35. RESULTS AND ANALYSIS 10
0
Partial SINR (Uncorr)
All SINR (Uncorr)
Partial SINR (HH)
‘Partial-ESINR’ vs.
Bit Error Rate (BER)
-1 All SINR (HH)
10
‘All-ESINR’
Partial SINR (Full)
All SINR (Full)
-2
10
-3
10
-10 -5 0 5 10 15 20
Signal-to-Noise Ratio (SNR) in dB
10 10
5 5
Channel Gain (dB)
Channel Gain (dB)
0 0
-5 -5
Source Source
-10 -10
Interferer Interferer
ESINR Metric ESINR Metric
-15 ESINR Metric (Interferer) -15 ESINR Metric (Interferer)
DSA-ESINR DSA-ESINR
DSA-ChG (Interferer) DSA-ESINR (Interferer)
-20 -20
0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700
Subcarrier Subcarrier
(a) ‘Partial ESINR’ scheme (b) ‘All ESINR’ scheme

36. RESULTS AND ANALYSIS
Comparisons of sub-optimal allocation schemes
ETSI ‘Channel E’ SCM ‘Urban Micro’
0 0
10 10
DSA-ESINR DSA-ESINR
MGSS-ESINR MGSS-ESINR
DSA-Sch5 Uncorrelated DSA-Sch5
DSA-Sch1 DSA-Sch1
Bit Error Rate (BER)
Bit Error Rate (BER)
-1 -1
10 10
-2 -2
10 10
-3 -3
10 10
-4 -2 0 2 4 -5 0 5 10 15
Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB
0 0
10 10
DSA-ESINR
MGSS-ESINR
‘Fully’ correlated
DSA-Sch5
DSA-Sch1
Bit Error Rate (BER)
Bit Error Rate (BER)
-1 -1
10 10
-2 -2
10 10
DSA-ESINR
MGSS-ESINR
DSA-Sch5
-3 -3
DSA-Sch1
10 10
-5 0 5 10 15 20 25 -5 0 5 10 15 20 25
Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB

37. RESULTS AND ANALYSIS
• „DSA-ESINR‟ vs. „DSA-Scheme 5‟
• DSA-Scheme 5 suffer from propagation error due to
subcarrier separation (j parameter)
0 0
10 10
Sch5 M1 Sch5 M1
ESINR M1 ESINR M1
Sch5 M2 Sch5 M2
ESINR M2 ESINR M2
Bit Error Rate (BER)
Bit Error Rate (BER)
-1 -1
10 Sch5 M3 10 Sch5 M3
ESINR M3 ESINR M3
Sch5 M4 Sch5 M4
ESINR M4 ESINR M4
Sch5 M5 Sch5 M5
-2 -2
10 ESINR M5 10 ESINR M5
Sch5 M6 Sch5 M5
ESINR M6 ESINR M6
-3 -3
10 10
-10 -5 0 5 10 15 20 25 30 -10 -5 0 5 10 15 20 25 30 35 40
Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB
(a) ETSI ‘Channel E’ (b) SCM ‘Urban Micro’

38. RESULTS AND ANALYSIS
Effective correlation coefficient
Uncorrelated ‘Fully’ correlated
1
1
0.8
0.8
Effective Correlation Co-efficients
Effective Correlation Co-efficients
0.6
0.6
0.4
0.4
0.2
0.2
0
0
-0.2
-0.2
-0.4 DSA-ESINR
-0.4 DSA-ESINR
MGSS-ESINR
-0.6 MGSS-ESINR
DSA-Sch1 -0.6
DSA-Sch5 DSA-Sch1
-0.8
ChG (Before Allocation) -0.8 DSA-Sch5
-1 ChG (Before Allocation)
-5 0 5 10 15 20 25 30 -1
Signal-to-Noise Ratio (SNR) in dB -5 0 5 10 15 20 25 30
Signal-to-Noise Ratio (SNR) in dB

39. RESULTS AND ANALYSIS
1.00 1.0
DSA-ESINR DSA-ESINR
MGSS-ESINR MGSS-ESINR
0.83 DSA-Sch5 DSA-Sch5
0.8
DSA-Sch1 DSA-Sch1
p(correlation coefficient)
p(correlation coefficient)
h11&h12
0.67
0.6
0.5
0.4
0.33
0.2
0.17
0 0
-1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1
correlation coefficient correlation coefficient
Uncorrelated ‘Fully’ correlated
1.0 1
SNR= 10 dB SNR= 10 dB
0.9 SNR= 20 dB 0.9 SNR= 20 dB
SNR= 30 dB SNR= 30 dB
0.8 0.8
p(correlation coefficient)
p(correlation coefficient)
0.7 0.7
0.6 0.6
0.5 0.5
0.4 0.4
0.3 0.3
0.2
0.2
0.1
0.1
0
-1 -0.5 0 0.5 1 0
-1 -0.5 0 0.5 1
correlation coefficient
correlation coefficient
DSA-ESINR MGSS-ESINR

40. Self-Interference in a LTE
Downlink Transmission
Exploiting limited feedback and multiuser
diversity in a spatially correlated channels

41. LTE FUNDAMENTALS
• Advantages of LTE:
– Performance: @ 20 MHz BW offering up to 50 Mbps (UL) and
100 Mbps (DL)
– Reduced latency: „flat‟ network architecture
– Improved spectrum flexibility: 1.25 to 20 MHz
– Operational cost: SON
• Work based on 3GPP-LTE Rel. 8 [20]
• MIMO-OFDMA as the potential candidate
for 4G downlink technology
[20] Technical Specification Group Radio Access Network; (E-UTRA) and (EUTRAN): Physical Channels and
Modulation‟, 3GPP TS 36.211 V8.4.0, Sept 08. [Online]. Available: http://www.3gpp.org/ftp/Specs/html-info/36211.htm

42. LTE FUNDAMENTALS One radio frame, Tt= 307,200Ts= 10 ms
One subframe, Tslot= 15,360Ts = 0.5 ms
Slot #0 Slot #1 Slot #19
• Resource block (RB): a group of 12
One slot
Resource Block
NRB= Nsub×Nsym
resource elements
subcarriers, smallest element in LTE
OFDMA Subcarrier (Frequency)
• Short and long Cyclic Prefix (CP)
• 15 MCS schemes [20], only six
Resource Element
Nsub NRB ×Nsub
considered for simulation
Coding Coded bits Data bits Nominal Bit
Mode Modulation
Rate per carrier per time slot Rate (Mbps)
Nsym
1 QPSK ½ 2 7,600 15.2
OFDMA Symbol (Time)
2 QPSK ¾ 2 11,400 22.8
3 16-QAM ½ 4 15,200 30.4
1 frame (10 ms)
4 16-QAM ¾ 4 22,800 45.6
5 64-QAM ½ 6 22,800 45.6 1 subframe (1 ms) 1 slot (0.5 ms)
6 64-QAM ¾ 6 34,200 68.4 0 1 2 3 10 11 19
0 1 2 3 4 5 6 0 1 2 3 4 5 6
7 OFDM symbols
(short cyclic prefix)
cyclic prefixes
LTE generic frame structure

43. LIMITED FEEDBACK IN LTE
• Capacity gain can be achieved when Nt antennas
communicate with k users: MU-MIMO [21], another form
of SDMA
• Benefit from CSIT. Can be achieved by precoding
technique at the expense of feedback overhead –
challenging especially in a fast fading channel
• Limited
Rx1
feedback: provides
„incomplete‟ info on the channel Tx1 RxN UE1
Rx1
• Three types of feedback Tx2
RxN UE2
schemes in LTE: CQI, RI and eNodeB
Rx1
PMI TxM
RxN
UEk
[21] H. Weingarten, Y. Steinberg, S.Shamai, “The capacity region of the Gaussian MIMO broadcast channel”, IEEE
Proc. International Symposium on Information Theory, Vol. 52, No. 9, pp. 3936-3964, Sept. 2006

44. LIMITED FEEDBACK IN LTE
• Modification: average ESINR metric as the CQI feedback
to benefit allocation and feedback scheme
• UE only feeds back a single CQI for the preferred matrix
for each RB
• The preferred precoding matrix for a RB is chosen by
selecting the highest average SINR perceived by user
• eNodeB chooses the precoding matrix with highest sum
of avg. SINR of all the spatial subchannels
Preferred Preferred Alternative Alternative Preferred Total
Feedback
Layer 1 Layer 2 Layer 1 Layer 2 Matrix bits per
Scheme
CQI CQI CQI CQI Index RB
MU-MIMO
4 bits 4 bits 4 bits 4 bits 1 bit 17 bits
Full Feedback
MU-MIMO
Partial 4 bits 4 bits - - 1 bit 9 bits
Feedback
SU-MIMO
4 bits - 1 bit 5 bits
Feedback

45. LIMITED FEEDBACK IN LTE
• Precoding to achieve accurate CSIT
• DFT-based codebook precoding is considered [22]
• Amount of feedback increased as the spatial
subchannels, Q and codebook size, L increased
[22] D. Yang; L. Yang, L. Hanzo, “DFT-Based Beamforming Weight-Vector Codebook Design for Spatially Correlated
Channels in the Unitary Precoding Aided Multiuser Downlink”, 2010 IEEE International Conference on Communications.
ICC 2010, pp. 1-5, May 2010

46. RESULTS AND ANALYSIS
Performance of different feedback schemes in different
correlation scenarios
0 0
10 10
Bit Error Rate (BER)
Bit Error Rate (BER)
-1 -1
10 10
-2
Full MU, Uncorr -2 Partial MU, Uncorr
10 Partial MU, Uncorr 10 Full-MU, Uncorr
SU, Uncorr SU, Uncorr
SU, Full Corr Full MU, Full Corr
Partial MU, Full Corr Partial MU, Full Corr
-3
Full MU, Full Corr SU, Full Corr
-3
10 10
-10 -5 0 5 10 15 -8 -6 -4 -2 0 2 4 6 8
Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB
(a) DSA-Channel gain (b) DSA-ESINR

47. RESULTS AND ANALYSIS
Effect of codebook sizes
0 0
10 10
0
10
L=1 L=1 L=1
L=2 L=2 L=2
L=4 L=4 L=4
Uncorrelated
L=8
Bit Error Rate (BER)
Bit Error Rate (BER)
Bit Error Rate (BER)
-1 -1 L=8 -1
L=8
10 10 10
-2 -2 -2
10 10 10
-3
-3 10 -3
10 -10 -5 0 5 10 10
-10 -5 0 5 10 -10 -5 0 5 10
Signal-to-Noise Ratio (SNR) in dB
Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB
0 0
0 10 10
10 L=1 L=1
L=1 L=2
L=2
‘Fully’ correlated
L=2 L=4
L=4
L=4
Bit Error Rate (BER)
L=8
Bit Error Rate (BER)
L=8
Bit Error Rate (BER)
L=8 -1 -1
-1 10 10
10
-2
-2 -2 10
10 10
-3 -3
10 10
-3 10
-10 -5 0 5 10 15 20 -10 -5 0 5 10
-10 -5 0 5 10
Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB
Signal-to-Noise Ratio (SNR) in dB
SU-MIMO Partial MU-MIMO Full MU-MIMO

48. RESULTS AND ANALYSIS
Multiuser diversity between SU-MIMO and MU-MIMO
0
0 0 10
10 10 1U
1U
2U
2U
5U
5U
Uncorrelated
Bit Error Rate (BER)
10U
Bit Error Rate (BER)
Bit Error Rate (BER)
-1 10U -1
10 10
-1 10 25U
25U
50U 50U
1U
-2 -2 -2
10 2U 10 10
5U
10U
25U
-3 50U -3
10 10 -3
-10 -5 0 5 10 15 -10 -5 0 5 10 15 10
-10 -5 0 5 10 15
Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB
0 0
10
0 10 10
1U 1U
2U 2U
‘Fully’ correlated
5U 5U
Bit Error Rate (BER)
10U
Bit Error Rate (BER)
Bit Error Rate (BER)
-1 -1 -1
10U
10 10 25U 10 25U
50U 50U
1U
-2
10 2U -2
10 10
-2
5U
10U
25U
-3 50U
10 -3
-10 -5 0 5 10 15 10 -3
Signal-to-Noise Ratio (SNR) in dB -10 -5 0 5 10 15 10
Signal-to-Noise Ratio (SNR) in dB -10 -5 0 5 10 15
Signal-to-Noise Ratio (SNR) in dB
SU-MIMO Partial MU-MIMO Full MU-MIMO

49. RESULTS AND ANALYSIS
Effect of DFT-codebook sizes for allocation
based on channel gain metric
0 0
10 10
L=1 L=1
L=2 L=2
L=4 L=4
Bit Error Rate (BER)
L=8
Bit Error Rate (BER)
-1
-1
L=8
10 10
-2
10 10
-2
-3
10 10
-3
-10 -5 0 5 10 15 20 -10 -5 0 5 10 15 20
Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB
Partial MU-MIMO Full MU-MIMO

50. RESULTS AND ANALYSIS
Spectral efficiency
7
Full-MU, Uncorr
Partial-MU, Uncorr
6
SU, Uncorr
Spectral Efficiency, in b/s/Hz
Full-MU, Full Corr
5 Partial-MU, Full Corr
SU, Full Corr
4 SISO
3
2
1
0
-10 -5 0 5 10 15 20 25
Signal-to-Noise Ratio (SNR) in dB

51. FUTURE WORK
• Joint resource allocation: power, channel gain, bits,
SINR. Potential for green radio transmission
• Accurate channel model: Kronecker‟s does not
consider other multipath characteristics
• Adaptive MCS: Instead of fixed modulation at mult. Tx
• DSA-Scheme 5: Adaptive j parameter
• SINR Filtration/ equalization : ZF, SIC
• LTE feedback schemes: Effect of different feedback
schemes: CQI, RI, PMI.

52. CONCLUSIONS
• MIMO-OFDMA integration for future 4G downlink
transmission will benefit from better resistance against
ISI and improved spectral efficiencies
• However, SM relies on the linear dependence among the
input streams, resulting in self-interference
• DSA-ESINR effective to combat self-interference,
particularly in a highly correlated channel
• Combination with partial feedback scheme can achieve
high multiuser diversity gain

53. THANK YOU!