Rosdiadee Nordin, Mahamod Ismail, "Impact of Spatial Correlation towards the Performance of MIMO Downlink Transmissions", Proceedings of 18th Asia-Pasific Conference on Communications. APCC 2012, Oct. 2012

1. Impact of Spatial Correlation
towards the Performance of MIMO
Downlink Transmissions
Rosdiadee Nordin, Mahamod Ismail
Universiti Kebangsaan Malaysia

2. LAYOUT
• Introduction
• Problem Background
• Objectives
• Methodology
• Results & Analysis
• Conclusions & Future Work

3. INTRODUCTION
• MIMO, in combination with OFDMA for IMT-A
network, i.e. LTE-A vs. Mobile WiMAX
• MIMO as a form of diversity:
– Space-Time Coding: achieve maximum antenna
diversity & improve wireless link reliability
(Alamouti’s)
– Spatial Multiplexing: increase data rate, i.e. higher
spectral efficiencies (V-BLAST)
• OFDMA: allowing efficient and flexible resource
allocation

4. PROBLEM BACKGROUND
• MIMO gain dependent on
number of Tx-Rx antenna; 12
RBS=0.0,RMS=0.0
subjected to uncorrelated fading 10
RBS=0.4,RMS=0.4
RBS=0.5,RMS=0.5
• Spectral efficiency depends 8
RBS=0.0,RMS=0.9
RBS=0.9,RMS=0.0
capacity (bps/Hz)
strongly on the statistical 6
RBS=0.9,RMS=0.9
RBS=1.0,RMS=1.0
behavior of the spatial fading
correlation, known as self- 4
interference 2
• As the spatial correlation 0
-10 -5 0 5
SNR (dB)
10 15 20
increases, cross-correlation will
occur between the spatial **The effect of self-interference needs
subchannels to be reduced**

5. PROBLEM BACKGROUND
• MIMO suffers from self-interference, resulting in ill-
conditioned matrices, cause degradation of capacity.
• Previous works [5], [6] and [7] suggest the existence of a
correlation between antenna elements
• Potential capacity gain dependent on the multipath richness
– Fully correlated MIMO channel offers only single effective channel, i.e.
SISO
– Fully de-correlated channel offers multiple capacity benefits
[5] 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
[6] S. Catreux, P. F. Driessen, and L. J. Greenstein, “Attainable throughput of an interference-limited multiple-input multiple-output (MIMO)
cellular system,” IEEE Transactions on Communications, Vol. 49, No. 8, pp. 1307-1311, Aug. 2001.
[7] D.-S. Shiu, G. J. Foschini, M. J. Gans, and J. M. Kahn, “Fading correlation and its effect on the capacity of multielement antenna systems,”
IEEE Transactions on Selected Areas in Communications, Vol. 48, No. 3, Mar. 2000

6. PROBLEM BACKGROUND
• Factors contribute towards self-interference:
– Multipath angular spread [5]
– Separation between Tx and Rx [6]
– Antenna element spacing [9]
– Array orientation [10]
[5] 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
[6] S. Catreux, P. F. Driessen, and L. J. Greenstein, “Attainable throughput of an interference-limited multiple-
input multiple-output (MIMO) cellular system,” IEEE Transactions on Communications, Vol. 49, No. 8, pp. 1307-
1311, Aug. 2001.
[9] M. Chamchoy, S. Promwong, P. Tangtisanon, J. Takada, "Spatial correlation properties of multiantenna UWB
systems for in-home scenarios," IEEE International Symposium on Communications and Information
Technology, 2004. ISCIT 2004, Vol.2, pp. 1029-1032, Oct. 2004.
[10] A. Intarapanich, P. L. Kafle, R. J. Davies, A. B. Sesay, and J. McRory, “Spatial correlation measurements for
broadband MIMO wireless channels,” IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall, Vol.1, pp.
52-56, Sept. 2004.

7. OBJECTIVES
• To investigate the effect of self-interference
towards transmission in both MIMO schemes
• To compares the performance of both STC and
SM schemes in different correlation scenarios
by means of subcarrier allocation

8. METHODOLOGY
(1) Initialization
• Allows the user with lowest Set Pk,q=0 for all users, u =1,…, U ; Set Ck,s,q=0 for all users
u =1,…, U and spatial subchannels q={1,2,…, Q}; Set s =1
channel gain to have the
(2) Main process
next best subcarrier gain:
While Nq’≠0Nsub
fairness vs. error probability {
(a) Make a short list according to the users that have less
• Involves sorting, comparing power. Find the user u satisfying:
Pu,q  Pi,q for all i, 1  i  u
and simple arithmetic. (b) For the user u got in (a), Find sub-carrier n satisfying:
| hu,n,q| ≥ | hu,j,q| for all j N
• Ranks users from lowest to (c) Update Pu,q’ , Nq and Cu,s,q’ with the s from (b)
according to
highest channel gain Pu,q= Pu,q+ | hu,n,q|2
Nq= Nq − s
Ck,s,q = n
s = s +1
(d) Go to the next user in the short list got in (a) until all
users are allocated another subcarrier
}

9. SIMULATION ENVIRONMENT
1
0.9
0.8
0.7
Normalised power
0.6
0.5
0.4
0.3
0.2
0.1
200 300 400 500 600 700 800 900 1000
Excess delay (ns)
• Allows separate optimization at both ends
• Correlation properties at both Tx/Rx antennas
are independent of each other

10. RESULTS & ANALYSIS
• Robustness of the STBC scheme against the effect of self-
interference across all correlation scenarios
• Single data stream replicated and Tx over multiple antennas
• Redundant data streams are each encoded. Tx signal is
orthogonal; reducing self-interference & improving the reliability
of the receiver to distinguish between the multiple signals

11. RESULTS & ANALYSIS
• ML detection in Alamouti’s known to tolerate against
moderate level of correlation, which is approximately
up to correlation coefficient, RMIMO < 0.8 [15].
• However, the implementation complexity of ML
receiver needs to be considered
– For small MS with low-power requirement
– When the delay spread of the channel is large
• Complexity of a ML decoder depends on the number of
receive antennas and the constellation size of the
modulation scheme
• For an Nt×Nr MIMO system using M-QAM, the
complexity is in the order of MNr.
[15] D. Gore, R. W. Heath, and A. Paulraj, “Performance analysis of spatial multiplexing in correlated channels,”
IEEE Trans. Commun., 2002.

12. RESULTS & ANALYSIS
• Difference in SM performance occurs due to the
architecture of spatial multiplexing transmission:
– probability of the other spatial interferer to transmit the
same subcarrier to the desired signal is very high as the
spatial subchannel approaches full correlation
– MMSE decoder offers a suboptimal solution for
equalization - poor ability to reduce effect of interference
in the fully correlated channel.
– In a highly correlated channel, the presence of self-
interference becomes more dominant than the additive
noise, thus the symbols’ detection and decoding become a
difficult task to the MMSE receiver.

13. CONCLUSIONS & FUTURE WORKS
• Both STBC and SM suffer from self-interference, degree
of impairment increases as degree of spatial
correlation increases
• Effect of self-interference is highly dominant in SM
architecture compared to STBC
• Due to the cross paths that occur between the spatial
multiplexing of data streams
• Future Works:
– Minimization of self-interference, focusing on SM scheme
based on several allocation strategies
– Subcarrier switch-off optimization (green communication)

14. 감사합니다!