Clustered Network MIMO and Fractional Frequency Reuse for the Downlink in LTE-A Systems

Clustered Network MIMO and Fractional FrequencyReuse for the Downlink in LTE-A Systems

Ajay Thampi†, Simon Armour†, Zhong Fan‡, Dritan Kaleshi†

†Communication Systems and Networks Research Group, University of Bristol, UK‡Toshiba Research Europe, Telecommunications Lab, Bristol, UK

May 16, 2014

Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 1 / 18

Thanks to...

The U.K. Research Council and Toshiba for jointly funding my PhD underthe Dorothy Hodgkin Postgraduate Awards.


The Problem

Worldwide data traffic to grow 7-fold in the next 3 yearsI 66% of that traffic will be video

Operators deploying 4G LTE NetworksI Reduced cell sizeI Aggressive frequency reuse (Reuse factor → 1)

Major Performance Bottleneck: Inter-Cell Interference


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 downlinkF Multiple-access channel on the uplink

I Ideal solution if backhaul links have infinite capacityI 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 inefficient use of spectrum


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 ClusterI Clusters 1 to C − 1: Neighbouring Clusters

R: Cell RadiusDc : Boundary between Cluster-Centre and Cluster-Edge


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 cl(c)k : Length of data symbol for user k in cluster c

I Assumption is that l(c)k = NR ∀k , 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 =

B∑b=1

H(c,b)k T

(c,b)k x

(c)k︸︷︷︸

desired signal

+B∑

b=1

H(c,b)k

K (c)∑i=1,i 6=k

T(c,b)i x

(c)i︸︷︷︸

intra-cluster interference

+C−1∑

c=0,c 6=c

B∑b=1

H(c,b)k

K (c)∑j=1

T(c,b)j x

(c)j︸︷︷︸

inter-cluster interference

+n(c)k

(1)


Existing Approach - Helper Clusters

Intra-Cluster InterferenceI Block diagonalisation (BD) precoding technique

F More practical than DPC and provides interference-free channels [1]

Inter-Cluster InterferenceI 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?


Proposed Approach - Network MIMO + FFR (1/3)

Set cluster size B = 3

FFR applied in cluster-scale to cancel inter-cluster interferenceI Bandwidth Partitioning is load-dependent



Bandwidth PartitioningI Let:

F M(i)c : Number of cluster-centre users in cluster i

F M(i): Total number of users in cluster iF 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



Network MIMO with conventional FFR [2]

Proposed FFR scheme v/s Conventional FFRI 84% v/s 100% spectrum utilisationI 76.6% v/s 61.5% spectrum allocation for cluster-centre users (under

high load)

Location Classification: Use logistic regression approach in [3]I Employ Minimisation of Drive Test (MDT) reports specified in 3GPP

TS37.320


The Setup

Cell ParametersNumber of Cells 21

Cell Radius, R 1 km

Coordination Distance, Dc 350 m

MIMO ParametersNumber of Transmit Antennas, NT 4

Number of Receive Antennas, NR 2

Channel ModelCarrier Frequency 800 MHz

Fading Narrowband, Rayleigh

Power AllocationTotal Power Constraint, P 46 dBm

Algorithm Scaled Water Filling

SchedulingAlgorithm Proportional Fair

Window Size 100Ajay Thampi (University of Bristol) Clustered Network MIMO and FFR May 16, 2014 11 / 18

Results - Overall Sum Rate


Results - Cluster Edge Sum Rate


Results - Fairness


Results - Complexity

Channel State Information (CSI) ReductionI 71% reduction when compared to global coordinationI 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


Possible Future Directions

Performance study with imperfect CSI and better precodingtechniques

Clustered Network MIMO + FFR in a heterogeneous network


Thank you!

Q & A

[email protected]

http://ajaythampi.net

@thampiman


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 Classification in OFDMA-based FFRSystems

IEEE WoWMoM, pp. 1-9.


Engineering

Clustered Network MIMO and Fractional Frequency Reuse for the Downlink in LTE-A Systems