Chinese Journal of Electronics
Vol.23, No.2, Apr. 2014
An Enhanced IRC Algorithm for LTE Downlink
Receiver in Multi-cell Environment
∗
YAN Zhiting, HE Guanghui, MA Jun
(School of Microelectr onics, Shanghai Jiao Tong University, Shanghai 200240, China )
Abstract — This paper proposes an enhanced Interfer-
ence rejection combining (IRC) algorithm for Long term
evolution (LTE) downlink receiver in multi-cell communi-
cation systems. In this algorithm, a proper Multiple input
multiple output (MIMO) receive method is adopted ac-
cording to Generalized likelihood ratio test (GLRT) inter-
cell interference detection. Iteration between channel es-
timation and data detection is carried out to improve the
performance of IRC algorithm. Simulation results show
that this proposed algorithm can effectively detect inter-
cell interference and improve Block error rate (BLER)
performance and channel estimation Mean squared error
(MSE) compared to non-iterative IRC algorithm, making
it suitable for LTE downlink receiver in multi-cell cellular
systems.
Key words — Long term evolution (LTE), Inter-cell
interference, Interference rejection combing (IRC), Gen-
eralized likelihood ratio test (GLRT).
I. Introduction
Long term evolution (LTE) system is designed to achieve a
frequency reuse factor of one in order to utilize the maximum
bandwidth available in each cell. Due to the reuse of the same
frequency band among neighboring cells, these cellular sys-
tems suffer from inter-cell interference, especially for users at
cell edges. Inter-cell interference coordination (ICIC), power
control and adaptive scheduling are promising approaches to
deal with such interference at base stations
[1]
.
In order to suppress inter-cell interference at terminals, In-
terference rejection combing (IRC) algorithm is investigated in
several works
[2]−[6]
. The purpose of IRC algorithm is to exploit
spatial correlation of different antennas to suppress inter-cell
interference
[2]
. In Ref.[3], it is verified that IRC algorithm
performs poorer than other linear MIMO receive algorithms
in channels without inter-cell interference, because spatial co-
variance matrix of channel noise can not be estimated precisely
using limited numbers of pilot subcarriers in LTE systems. To
the best of our knowledge, there is no feasible solution to this
problem at present.
In addition, IRC algorithm is highly sensitive to the qual-
ity of channel and inter-cell interference covariance matrix
estimation
[4]−[6]
. Unfortunately, most of the existing works on
channelestimation
[7]−[10]
and data detection
[11]−[14]
for LTE
downlink receiver do not take inter-cell interference into ac-
count. Although decision-directed method is an attractive ap-
proach to improve the performance of channel estimation and
data detection simultaneously
[15]
, it also does not consider in-
terference from other cells. In short, all these receive algo-
rithms do not address inter-cell interference problem in LTE
systems.
In this paper, we proposed an enhanced IRC algorithm for
LTE downlink receiver in multi-cell environment. To solve the
problem of performance degradation in channels without inter-
cell interference, a proper MIMO receive method is adopted ac-
cording to Generalized likelihood ratio test (GLRT) inter-cell
interference detection. Specifically, this method first assesses
interference detection result and then adapts the data detec-
tion between linear algorithm and iterative IRC algorithm.
Moreover, to improve the estimation performance of channel
response and spatial covariance matrix, an iterative IRC algo-
rithm is proposed. Through iteration between channel estima-
tion and data detection, the accuracy of channel estimation is
increased using channel information from data detection.
Notations Boldface letters identify vectors or matrices.
(·)
∗
,(·)
T
and (·)
H
represent complex conjugate, transpose, and
Hermitian. E(·) denotes the expectation, I indicate the iden-
tity matrix.
II. System Models
Consider a LTE downlink MIMO spatial multiplexing
model with K subcarriers, N
T
transmit and N
R
receive an-
tennas. Assume L is the number of OFDM symbols in a LTE
subframe. The system model is expressed as follows.
Y (k, l)=H
s
(k.l)X
s
(k, l)+n(k, l)(1)
where X
s
(k, l) denotes N
T
×1transmitdatavectoratthekth
subcarrier of the lth OFDM symbol of a subframe, which is
called a resource element in LTE system. H
s
(k, l)isN
R
× N
T
MIMO channel matrix with each element as complex Gaus-
sian random variable and is assumed constant during a LTE
subframe. And n(k, l)isN
R
× 1 independent identical dis-
tribution (i.i.d.) cyclic symmetric complex Gaussian random
vector with covariance matrix of σ
2
I.
Three well-known linear MIMO receive algorithms are
Matching filter (MF), Zero forcing (ZF) and Minimum mean-
∗
Manuscript Received May 2013; Accepted Jul. 2013. This work is supported by the National Natural Science Foundation of China
under Grant (No. 61306026).
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