The document presents a study on using clustered network MIMO and fractional frequency reuse (FFR) to reduce inter-cell interference in LTE-A systems. It proposes partitioning bandwidth between cell-center and cell-edge users in a load-dependent way for each cluster. This achieves 84% spectrum utilization and higher allocation to cell-center users compared to conventional FFR. Simulation results show improved sum rate and cell-edge sum rate, as well as reduced complexity from less channel state information sharing compared to other approaches.

1. Clustered Network MIMO and Fractional Frequency
Reuse for the Downlink in LTE-A Systems
Ajay Thampiy, Simon Armoury, Zhong Fanz, Dritan Kaleshiy
yCommunication Systems and Networks Research Group, University of Bristol, UK
zToshiba Research Europe, Telecommunications Lab, Bristol, UK
May 16, 2014
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 1 / 18

2. Thanks to...
The U.K. Research Council and Toshiba for jointly funding my PhD under
the Dorothy Hodgkin Postgraduate Awards.
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 2 / 18

3. The Problem
Worldwide data trac to grow 7-fold in the next 3 years
I 66% of that trac will be video
Operators deploying 4G LTE Networks
I Reduced cell size
I Aggressive frequency reuse (Reuse factor ! 1)
Major Performance Bottleneck: Inter-Cell Interference
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 3 / 18

4. Possible Solutions
Network MIMO (aka CoMP)
I Base stations pooled together to form a Virtual MIMO system
F Data and channel states are shared
I Interference channel becomes:
F Broadcast channel on the downlink
F Multiple-access channel on the uplink
I Ideal solution if backhaul links have in

5. nite capacity
I Realistically, global coordination is unscalable
Fractional Frequency Reuse (FFR)
I Split the spectrum into two bands:
F Band 1: Cell-Centre (Reuse factor = 1)
F Band 2: Cell-Edge (Reuse factor 1)
I Cancels interference entirely but inecient use of spectrum
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 4 / 18

6. Clustered Network MIMO - System Model (1/2)
Scalable Network MIMO
C Clusters, each of size B (Here, C = 7 and B = 3)
I Cluster 0: Home Cluster
I Clusters 1 to C 1: Neighbouring Clusters
R: Cell Radius
Dc : Boundary between Cluster-Centre and Cluster-Edge
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 5 / 18

7. Cluster Network MIMO - System Model (2/2)
NT : Number of transmit antennas (at the base station)
NR: Number of receive antennas (for each user in the cell)
K(c): Number of users in cluster c
l (c)
k : Length of data symbol for user k in cluster c
I Assumption is that l (c)
k = NR 8k; c
x(c)
k : NR 1 transmitted signal vector for user k in cluster c
y(c)
k : NR 1 received signal vector for user k in cluster c
y(c)
k =
XB
b=1
H(c;b)
k T(c;b)
k x(c)
k
| {z }
desired signal
+
XB
b=1
H(c;b)
k
K(c) X
i=1;i6=k
T(c;b)
i x(c)
i
| {z }
intra-cluster interference
+
CX1
^c=0;^c6=c
XB
^b=1
H(^c;^b)
k
K(^c) X
j=1
T(^c;^b)
j x(^c)
j
| {z }
inter-cluster interference
+n(c)
k
(1)
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 6 / 18

8. Existing Approach - Helper Clusters
Intra-Cluster Interference
I Block diagonalisation (BD) precoding technique
F More practical than DPC and provides interference-free channels [1]
Inter-Cluster Interference
I Get neighbouring clusters to help the edge users in the home cluster
F Not guaranteed to cancel interference
I Main idea is to increase the cluster size (B = 7 worked in [1])
Can we do better?
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 7 / 18

9. Proposed Approach - Network MIMO + FFR (1/3)
Set cluster size B = 3
FFR applied in cluster-scale to cancel inter-cluster interference
I Bandwidth Partitioning is load-dependent
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 8 / 18

10. Proposed Approach - Network MIMO + FFR (2/3)
Bandwidth Partitioning
I Let:
F M(i)
c : Number of cluster-centre users in cluster i
F M(i): Total number of users in cluster i
F W: Total available bandwidth
I Bandwidth allocated for cluster-centre users in cluster i :
W(i)
c =
M(i)
c
M(i)
!
W
'
(2)
I Bandwidth allocated for cluster-edge users in cluster i :
W(i)
e =
$
W W(i)
c
3
!%
(3)
I Choose largest W(i)
e and corresponding W(i)
c for all clusters
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 9 / 18

11. Proposed Approach - Network MIMO + FFR (3/3)
Network MIMO with conventional FFR [2]
Proposed FFR scheme v/s Conventional FFR
I 84% v/s 100% spectrum utilisation
I 76.6% v/s 61.5% spectrum allocation for cluster-centre users (under
high load)
Location Classi

12. cation: Use logistic regression approach in [3]
I Employ Minimisation of Drive Test (MDT) reports speci

13. ed in 3GPP
TS37.320
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 10 / 18

14. The Setup
Cell Parameters
Number of Cells 21
Cell Radius, R 1 km
Coordination Distance, Dc 350 m
MIMO Parameters
Number of Transmit Antennas, NT 4
Number of Receive Antennas, NR 2
Channel Model
Carrier Frequency 800 MHz
Fading Narrowband, Rayleigh
Power Allocation
Total Power Constraint, P 46 dBm
Algorithm Scaled Water Filling
Scheduling
Algorithm Proportional Fair
Window Size 100
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 11 / 18

15. Results - Overall Sum Rate
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 12 / 18

16. Results - Cluster Edge Sum Rate
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 13 / 18

17. Results - Fairness
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 14 / 18

18. Results - Complexity
Channel State Information (CSI) Reduction
I 71% reduction when compared to global coordination
I 14% reduction when compared to the helper approach
Execution time (in milliseconds)
B = 21
(Global)
B = 7
(Helper)
B = 3
(FFR-Cell)
B = 3
(FFR-
Cluster)
239.75 10.43 3.37 2.01
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 15 / 18

19. Possible Future Directions
Performance study with imperfect CSI and better precoding
techniques
Clustered Network MIMO + FFR in a heterogeneous network
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 16 / 18

20. Thank you!
Q A
ajay.thampi@bristol.ac.uk
http://ajaythampi.net
@thampiman
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 17 / 18

21. References
[1] J.Zhang; et al (2009)
Networked MIMO with Clustered Linear Precoding
IEEE Transactions on Wireless Communications, vol. 8, no. 4, pp. 1910-1921.
[2] L.C.Wang; et al (2011)
3-cell network MIMO architectures with sectorization and FFR
IEEE Journal on Selected Areas in Communications, vol. 29, no. 6, pp. 1185-1199.
[3] A.Thampi; et al (2013)
A Logistic Regression Approach to Location Classi

22. cation in OFDMA-based FFR
Systems
IEEE WoWMoM, pp. 1-9.
Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 18 / 18