Aalborg Universitet
On the Potential of Interference Rejection Combining in B4G Networks
Tavares, Fernando Menezes Leitão; Berardinelli, Gilberto; Mahmood, Nurul Huda; Sørensen,
Troels Bundgaard; Mogensen, Preben
Published in:
Vehicular Technology Conference (VTC Fall), 2013 IEEE 78th
DOI (link to publication from Publisher):
10.1109/VTCFall.2013.6692318
Publication date:
2013
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Tavares, F. M. L., Berardinelli, G., Mahmood, N. H., Sørensen, T. B., & Mogensen, P. (2013). On the Potential of
Interference Rejection Combining in B4G Networks. In Vehicular Technology Conference (VTC Fall), 2013 IEEE
78th (pp. 1-5). IEEE. I E E E V T S Vehicular Technology Conference. Proceedings
https://doi.org/10.1109/VTCFall.2013.6692318
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On the Potential of Interference Rejection
Combining in B4G Networks
Fernando M. L. Tavares, Gilberto Berardinelli, Nurul H. Mahmood, Troels B. Sørensen, and Preben Mogensen
Department of Electronic Systems, Aalborg University
Niels Jernes Vej 12, 9220 Aalborg Øst, Denmark
ft@es.aau.dk
Abstract—Beyond 4th Generation (B4G) local area networks
will be characterized by the dense uncoordinated deployment
of small cells. This paper shows that inter-cell interference,
which is a main limiting factor in such networks, can be
effectively contained using Interference Rejection Combining
(IRC) receivers. By simulation we investigate two significantly
different interference scenarios with dense small cell deployment.
The results show that IRC brings considerable improvement
in outage as well as in peak and median throughputs in both
scenarios, and thus has a big potential as a capacity and coverage
enhancing technique for B4G. The IRC gain mechanism depends
strongly on the interference scenario and to some extent on
the use of frequency reuse. These results are achieved with
no coordination among cells and suggests that Multiple Input
Multiple Output (MIMO) rank adaptation and IRC can be
performed independently.
I. INTRODUCTION
In the last decades, several generations of Radio Access
Networks (RANs) have been designed to cope with the grow-
ing demand for wireless services. A new disruptive system has
emerged approximately every 10 years to alleviate backward
compatibility problems and to take advantage of the evolution
of the technology components. Considering that the speci-
fications of the Long Term Evolution - Advanced (LTE-A)
radio standard were submitted in 2010, a novel Beyond 4
th
Generation (B4G) RAN is then expected to emerge in the
market around 2020 [1].
This new RAN should be designed to support the massive
deployment of small cells, since this type of deployment
is foreseen as a solution for meeting the future capacity
expansion requirements [1]. As deployments become denser,
their uncoordinated nature will inevitably aggravate the inter-
cell interference problem, causing considerable impact on the
network performance. The allocation of orthogonal spectrum
resources to cells that strongly interfere with each other is
often considered as the solution for this problem [2], [3].
With the evolution of electronic hardware and Multiple
Input Multiple Output (MIMO) techniques, inter-cell inter-
ference suppression techniques, whose application was pre-
viously limited by their large computational burden, may now
be cost-effectively implemented in receivers. It is reasonable to
believe that this kind of receivers can offer high performance
gains in interference limited scenarios, but, to the best of our
knowledge, all the system level performance evaluation studies
on the topic focus on macro cell and heterogeneous LTE and
LTE-A network scenarios [4]–[7].
Different baseband processing techniques may be used to
suppress the inter-cell interference. For instance, Successive
Interference Cancellation (SIC) and Parallel Interference Can-
cellation (PIC) techniques decode both the desired and the
interfering signal to cancel their mutual interference contribu-
tion [8]. Conversely, Interference Rejection Combining (IRC)
is a linear combining technique that relies on multiple receive
antennas and the estimate of the interfering channels to project
the received signals on a subspace in which the Mean Square
Error (MSE) is minimized [9]. IRC is attractive given its
simplicity and maturity, and it represents a straightforward
add-on to the known Minimum Mean Square Error (MMSE)
detector, which is now considered the baseline detector in LTE
networks [10].
In this paper, we present the first system level downlink
performance evaluation of inter-cell interference suppression
for different local area small cell B4G scenarios. Specifically,
we consider scenarios with dense small cell deployment and
two modes of operation: one with closed subscriber mode
of relevance for office buildings (or private apartments) and
one with open subscriber mode of relevance for public hot
spots. We use a signal model that includes spatial multiplexing
precoding with multiple data layers (both for the desired
and the interfering signals) to verify their effects on the
interference rejection capabilities of IRC and contrast it with
the use of different frequency reuse schemes. Our ultimate
goal is to address the effective potential of this detector on
the performance of B4G networks, providing information that
will help guide the design of this new RAN.
The paper is organized as follows. We first describe the
signal model used in the simulations in Section II and the
details related to the simulation setup in Section III; we present
and analyse the simulation results in Section IV; finally, we
draw our conclusions in Section V.
II. S
IGNAL MODEL
In this section, we present the analytical signal model of
the detectors that are used in our system evaluation.
We assume that the generic i-th network node is equipped
with N
tx
transmit antennas and N
rx
receive antennas, and
can transmit 1 ≤ N
streams
i
≤ min(N
rx
,N
tx
) data streams.
The number of transmitted streams is also often referred as
transmission rank. For simplicity, we present the system model
978-1-4673-6187-3/13/$31.00 ©2013 IEEE
for a generic Orthogonal Frequency Division Multiplexing
(OFDM) frequency subcarrier.
Let us denote with s
i
the N
streams
i
×1 data column vector of
the i-th node. The vector s
i
is mapped over the N
tx
antennas
by the N
tx
× N
streams
i
precoding matrix C
i
. Let us assume
that the subscript D denotes the desired signal at the receiver
side, and the subscript I
q
the q-th interfering signal. After
transmission over the fading channel, the N
rx
× 1 frequency
domain received column vector at a particular receiver can
then be expressed as
r =
˜
H
D
s
D
+
˜
H
I
s
I
+ n (1)
where
• n is the N
rx
×1 Additive White Gaussian Noise (AWGN)
contribution vector with power σ
2
0
;
•
˜
H
D
and
˜
H
I
q
are the equivalent channel matrices which
include the precoding matrices, i.e.
˜
H
D
= H
D
C
D
and
˜
H
I
q
= H
I
q
C
I
q
, with H
D
and H
I
q
being the N
rx
× N
tx
fading channel matrices;
• s
I
and
˜
H
I
represent the concatenation of the N
I
received
interfering signals and the concatenation of their equiva-
lent N
I
channels, respectively, i.e.
s
I
=[s
T
I
1
...s
T
I
q
...s
T
I
N
I
]
T
(2)
˜
H
I
=[
˜
H
I
1
...
˜
H
I
q
...
˜
H
I
N
I
] (3)
where (·)
T
denotes the transpose operator.
Let us also define the generic MMSE combining ma-
trix [11]:
W =(
ˆ
H
D
ˆ
H
H
D
+ R
n
)
−1
ˆ
H
D
(4)
where (·)
H
is the hermitian operator and
ˆ
H
D
represents the
estimated equivalent channel matrix of the desired signal.
The desired ˆs
D
is then estimated by using the combining
matrix W:
ˆs
D
= W
H
r (5)
The following detectors can be specified according to the
nature of the matrix R
n
:
• MMSE - Interference Rejection Combining (MMSE-IRC):
R
n
= E
ˆ
H
I
ˆ
H
H
I
+ σ
2
0
I
N
rx
(6)
where I
N
rx
denotes the N
rx
× N
rx
identity matrix.
• MMSE - Maximal Ratio Combining (MMSE-MRC):
R
n
= diag([m
1
···m
N
rx
]) (7)
where
m
z
= E
N
I
q =1
N
streams
q
k=1
ˆ
H
I
q
(z,k)
2
+ σ
2
0
(8)
with
ˆ
H
I
q
(z,k)
corresponding to the element in the z-th row
and the k-th column of
ˆ
H
I
q
.
While in the MMSE-MRC detector the matrix R
n
can be
computed by estimating the total interference plus noise power
TABLE I
P
HYSICAL LAY E R ASSUMPTIONS
Physical Layer Model [1]
Spectrum Allocation 200 MHz at 3.5 GHz
Frame Duration 0.25 ms
Access Scheme
Downlink OFDMA
3000 subcarrriers 60 kHz each
15 subcarriers per PRB
Transmission Power 20 dBm
Receiver Noise Figure 9 dB
MIMO Scheme
Closed-loop SU-MIMO with
dynamic rank and precoder adaptation
Spectral Efficiency Model
Maximum Spectral Efficiency 6 bits/s/Hz 64 QAM (1/1)
Error Vector Magnitude 5% SINR
max
≈ 26dB
at each receive antenna, R
n
in the MMSE-IRC detector cor-
responds to the estimated covariance matrix of the interfering
signals. Therefore, MMSE-IRC assumes knowledge of each
interfering channel at each antenna which may impose strict
requirements on the system design since each node would
need to discriminate the reference sequences sent by multiple
interferers. We assume here that such a system design is
possible, and consider it to be a topic for future work.
III. S
IMULATION SETUP
As mentioned in the introduction, the usage of IRC detectors
is foreseen as particularly beneficial in scenarios characterized
by a dense uncoordinated deployment of small cells. This
paper aims at addressing such potential with an extensive
system level evaluation. In this section, we describe in details
the physical layer assumptions, scenarios and simulation setup
used in our simulation campaign.
A. Physical Layer Assumptions
We assume ideal channel estimation of both desired and
interfering signals based on the aforementioned reference
sequences. Rank and precoding adaptation feedback with no
errors and one frame delay is assumed. The precoding matrices
used are those defined in [12] for downlink closed-loop single-
user MIMO in LTE, and are applied per Physical Resource
Block (PRB) in frequency domain for the assumed OFDM
based system [1].
We calculate the Signal-to-Interference-plus-Noise Ratio
(SINR) for the j-th stream as follows [11]:
SINR
j
=
W
H
(j)
H
D
(j)
H
H
D
(j)
W
(j)
W
H
(j)
(
¯
H
D
(j)
¯
H
H
D
(j)
+ H
I
H
H
I
+ σ
2
0
I
N
rx
)W
(j)
(9)
where A
(j)
denotes the j-th column of matrix A and
¯
A
(j)
is
the matrix obtained by removing the j-th column from matrix
A.
10 m
10 m
10 m
(a) Scenario A - Indoor Office
10 m
10 m
(b) Scenario B - Indoor Hotspot
Fig. 1. Simulation Scenarios
The SINR values are then adjusted using the following Error
Vector Magnitude (EVM) model to account for transceiver
implementation imperfections.
SINR
evm,j
=
SINR
j
· SINR
max
SINR
j
+ SINR
max
(10)
Based on the resulting SINR we calculate the corresponding
data rate using the Shannon formula, with maximum spectral
efficiency limited to 6 bits/s/Hz (uncoded 64QAM modula-
tion). All streams are added, resulting in the total data rate
per User Equipment (UE). Table I summarizes these physical
layer details.
B. Simulation Scenarios
Two simulation scenarios were selected for this study.
Scenario A is an indoor office scenario used for the study
of femtocells [13]. This scenario is depicted in Figure 1(a).
It consists of two office buildings, each located at one side
of a 10 meter wide street. Each building is modelled as two
rows of 10 square offices. For simplicity, only one floor was
simulated with one UE and one Access Point (AP) randomly
placed in each office. Each office may have one active cell
under Closed Subscriber Group (CSG) access mode, i.e. the
UE can only connect to the AP in the same office and not to
any of the neighbour’s APs. Large scale propagation effects
(pathloss and shadowing) are calculated using the 3GPP Dual
Stripe model [13].
Figure 1(b) depicts Scenario B. This scenario simulates an
indoor hotspot scenario, similar to an airport check-in hall or a
large conference hall, for example. The total area of the hall is
divided in square areas and one AP is installed in the center
TABLE II
S
IMULATION SETUP
Scenario A - Indoor Office
Access Mode Closed Subscriber Group (CSG)
Data Generation Full Buffer Traffic
Path Loss
3GPP Dual Stripe Model
45 dB Minimum Coupling Loss
Wall Loss
Internal Walls 5 dB attenuation
External Walls 10 dB attenuation
Shadowing Std. Deviation
Serving Cell 6 dB
Other Cells 8 dB
Fast Fading
WINNER II CDL Model
Indoor Office (A1) - 3 Km/h
Antenna Configuration
Uniform Linear Array (ULA)
4 antenna elements (0.5λ spacing)
Scenario B - Indoor Hotspot
Access Mode Open Subscriber Group (OSG)
Data Generation Full Buffer Traffic
Path Loss
WINNER II Indoor Hotspot (B3) Model
70 dB Minimum Coupling Loss
Shadowing Std. Deviation
Line of Sight 3 dB
Non Line of Sight 4 dB
Fast Fading
WINNER II CDL Model
Indoor Hotspot (B3) - 3 Km/h
Antenna Configuration
Uniform Linear Array (ULA)
4 antenna elements (0.5λ spacing)
of each of them, summing up 40 APs. In this scenario, the
user may connect to any of the available APs, i.e. the network
operates in Open Subscriber Group (OSG) access mode. One
UE is randomly placed in each square area and each UE selects
which AP to connect to based on the highest received power.
In case an AP does not serve any UE, it is switched off. In
this scenario, pathloss and shadowing are calculated according
to the WINNER II Indoor Hotspot (B3) channel model [14].
Small scale fading samples used in the simulation were
computed using the WINNER II channel model (Indoor Of-
fice (A1) for Scenario A and the Indoor Hotspot (B3) for
Scenario B) [14]. We assume uniform linear antenna arrays
with four elements separated by λ/2 in both APs and UEs.
In both scenarios, we assume 3 Km/h mobility that may be
due to device mobility or other objects moving in the same
area causing the channel to change. Table II presents further
details on the simulation scenarios.
C. Simulation Results
The network downlink performance was evaluated using a
quasi-static system level simulator. The statistical reliability
of the simulations is ensured by collecting results from 500
0 500 1000 1500 2000 2500 3000 3500 4000
0
10
20
30
40
50
60
70
80
90
100
Data Transmission Rate [Mbps]
ECDF [%]
MRC R1
IRC R1
MRC R2
IRC R2
MRC R4
IRC R4
(a) Scenario A - Indoor Office
0 200 400 600 800 1000
0
10
20
30
40
50
60
70
80
90
100
Data Transmission Rate [Mbps]
ECDF [%]
MRC R1
IRC R1
MRC R2
IRC R2
MRC R4
IRC R4
(b) Scenario B - Indoor Hotspot
Fig. 2. ECDFs showing the data transmission rates in Mbps for both detector types (MMSE-MRC and MMSE-IRC) and different frequency reuse schemes.
TABLE III
DATA RATES -SCENARIO A[MBPS]
Outage MMSE-MRC MMSE-IRC Gains
R1 91.8 337.4 (+267.5%)
R2 314.7 573.6 (+82.2%)
R4 543.4 697.1 (+28.3%)
Median MMSE-MRC MMSE-IRC Gains
R1 837.5 1261.2 (+50.6%)
R2 1178.4 1402.4 (+19.0%)
R4 1026.0 1029.6 (+0.3%)
Peak MMSE-MRC MMSE-IRC Gains
R1 2576.7 2804.2 (+8.8%)
R2 2058.3 2065.1 (+0.3%)
R4 1080.0 1080.0 -
snapshots. Each snapshot evaluates a time span of 50 frames
in which the fast fading channel values are updated every
frame, but pathloss and shadowing remain constant. An
Empirical Cumulative Distribution Function (ECDF) is then
calculated using the throughput of all cells in all snapshots.
Three key performance indicators (KPIs) were extracted from
the ECDFs, namely peak (95%-tile), median (50%-tile) and
outage (5%-tile) data rates.
In both network scenarios, we simulate different frequency
reuse schemes by splitting the total bandwidth and assigning
only part of the Physical Resource Blocks (PRBs) to each cell.
We simulate the scenarios with Reuse 1 (R1), Reuse 2 (R2)
and Reuse 4 (R4). In the case of R1, all PRBs are used by all
cells, but in the case of R2 and R4, each cell will use only
a half and a quarter of all PRBs, respectively. In these two
cases, the frequency allocation follows a geometrical pattern
that maximizes the distance between two cells using the same
set of PRBs.
TABLE IV
D
ATA RATES -SCENARIO B[MBPS]
Outage MMSE-MRC MMSE-IRC Gains
R1 113.7 142.1 (+25.0%)
R2 104.7 139.8 (+33.5%)
R4 87.4 131.9 (+51.0%)
Median MMSE-MRC MMSE-IRC Gains
R1 233.1 287.9 (+23.5%)
R2 201.7 253.3 (+25.6%)
R4 162.8 222.4 (+36.6%)
Peak MMSE-MRC MMSE-IRC Gains
R1 435.4 536.6 (+23.3%)
R2 346.3 428.0 (+23.6%)
R4 272.3 353.3 (+29.8%)
IV. PERFORMANCE EVA L UAT IO N
In this section, we present the numerical simulation results
using different combinations of receiver type and planned
frequency reuse schemes. First, we discuss the results for
Scenario A. Figure 2(a) displays the network throughput per-
formance assuming MMSE-MRC and MMSE-IRC detectors,
for different frequency reuse patterns, while Table III presents
the KPIs and the relative gains of MMSE-IRC over MMSE-
MRC for all Scenario A simulation cases. The MMSE-IRC
detector shows considerable gain in terms of outage data
rate with respect to MMSE-MRC when R1 is adopted. Such
improvement diminishes with higher reuse factors, due to the
lower total interference to be rejected. However, the relative
data rate gains that MMSE-IRC provides over MMSE-MRC
are very large, with improvements of 267.5%, 82.2% and
28.3% in R1, R2 and R4 cases, respectively.
The large data rate gain of MMSE-IRC in scenario A is due
to its capability of rejecting the strongest interfering signals.
![TABLE IV DATA RATES - SCENARIO B [MBPS]](/figures/table-iv-data-rates-scenario-b-mbps-2j3z3s2x.webp)
![TABLE III DATA RATES - SCENARIO A [MBPS]](/figures/table-iii-data-rates-scenario-a-mbps-1evuubkq.webp)
