Impact of Clustering in Indoor MIMO Propagation Using a Hybrid Channel Model
EURASIP Journal on Applied Signal Processing
Impact of Clustering in Indoor MIMO Propagation Using a Hybrid Channel Model
Zhongwei Tang 0
Ananda Sanagavarapu Mohan 0
0 Microwave and Wireless Technology Research Laboratory, Information and Communication Group, Faculty of Engineering, University of Technology , Sydney, L24/B1, P.O. Box 123, Broadway NSW 2007 , Australia
The clustering of propagating signals in indoor environments can influence the performance of multiple-input multiple-output (MIMO) systems that employ multiple-element antennas at the transmitter and receiver. In order to clarify the effect of clustering propagation on the performance of indoor MIMO systems, we propose a simple and efficient indoor MIMO channel model. The proposed model, which is validated with on-site measurements, combines the statistical characteristics of signal clusters with deterministic ray tracing approach. Using the proposed model, the effect of signal clusters and the presence of the line-of-sight component in indoor Ricean channels are studied. Simulation results on channel efficiency and the angular sensitivity for different antenna array topologies inside a specified indoor scenario are also provided. Our investigations confirm that the clustering of signals significantly affects the spatial correlation, and hence, the achievable indoor MIMO capacity.
and phrases; angle sensitivity; channel efficiency; indoor propagation; signal clusters; MIMO; Ricean K factor; ray tracing
1. INTRODUCTION
The multiple-input multiple-output (MIMO) technique is
being tipped as one of the most significant breakthroughs in
wireless communications for achieving high data-rates
without increasing the channel bandwidth [
1, 2, 3, 4
]. In view
of its significance, the MIMO technique is considered for
inclusion into the forthcoming IEEE 802.11n WLAN standard.
MIMO systems have the ability to turn multipath
propagation into a benefit for users by employing multiple antennas
at both the transmitter and receiver to exploit multipath
fading, in order to maximize data throughput. The underlying
mathematical nature of MIMO, where the data is transmitted
over a matrix rather than a vector channel, creates new and
enormous opportunities beyond just diversity or array gain
benefits. This has prompted new research on channel
modelling, antenna design, coding schemes and signal processing,
and so forth.
In MIMO systems, the channel transfer matrix is a key
component that includes the coupling information between
the transmitter and the receiver and their interaction with the
surrounding physical environment, through the spatial and
angular features of RF propagation. It has been reported that
the correlation of the channel transfer matrix due to
directional multipath propagation tends to decrease MIMO
performance [
5, 6, 7
] for both indoor and outdoor MIMO
systems, when time diversity is not considered [
8
]. Thus, the
characteristics of the transmit and receive arrays, such as
antenna polarization [
9
], antenna element separation [
10
], and
array topology and orientations [
11
] can play a major role in
determining the achievable MIMO capacity. It has also been
reported [
12, 13
] that the presence of the line-of-sight (LOS)
component in Ricean channels also reduces the achievable
MIMO capacity.
As the antenna characteristics and channel correlations
affect the achievable spectrum efficiency, antenna selection
assumes importance for obtaining the optimized capacity
[
14, 15
]. On the other hand, in indoor propagation
environments, it has been well-established that multipath waves
tend to be clustered in both angular and temporal
domains [
16, 17, 18, 19
]. Moreover, the clustering propagation
is found to be detrimental to indoor MIMO performance
as it increases the spatial correlation between subchannels
[
20, 21
]. When antenna arrays are employed at both the
transmitter and receiver in indoor environments, the
correlation between antenna elements is a function of the signal
clusters, whose characteristics are determined by the
physical features of a given indoor environment as well as the
locations of the transmitter and receiver. Since antennas are
key components in MIMO systems, it is important to
understand the impact of antenna array topology and
orientation on achievable capacity in clustering indoor
environments. Many realistic indoor environments are Ricean
scenarios since it is common for a strong line-of-sight
component between the transmitter and receiver to exist. Thus an
investigation on the effect of Ricean K factor on the
achievable MIMO capacity assumes practical importance. Further,
the subchannel efficiency in indoor environments is not fully
addressed. Therefore, a comprehensive investigation on the
impact of signal clustering on indoor MIMO performance is
expedient for the efficient design and deployment of
highperformance wireless systems. To this end, it is essential to
exploit the MIMO channel (...truncated)