08-08-2012, 12:57 PM
Channel Estimation and Equalization for Cooperative Communication
[attachment=30555]
Abstract
The revolutionary concept of space-time coding introduced in the last decade has
demonstrated that the deployment of multiple antennas at the transmitter allows for
simultaneous increase in throughput and reliability because of the additional degrees of
freedom offered by the spatial dimension of the wireless channel. However, the use of
antenna arrays is not practical for deployment in some practical scenarios, e.g., sensor
networks, due to space and power limitations.
A new form of realizing transmit diversity has been recently introduced under the name of
user cooperation or cooperative diversity. The basic idea behind cooperative diversity rests
on the observation that in a wireless environment, the signal transmitted by the source node is
overheard by other nodes, which can be defined as “partners” or “relays”. The source and its
partners can jointly process and transmit their information, creating a “virtual antenna array”
and therefore emulating transmit diversity.
Most of the ongoing research efforts in cooperative diversity assume frequency flat
channels with perfect channel knowledge. However, in practical scenarios, e.g. broadband
wireless networks, these assumptions do not apply. Frequency-selective fading and imperfect
channel knowledge should be considered as a more realistic channel model. The
development of equalization and channel estimation algorithms play a crucial element in the
design of digital receivers as their accuracy determine the overall performance.
Introduction
A quick glimpse of recent technological history reveals out that mobile communication
systems create a new generation roughly every 10 years. First-generation analogue systems
were introduced in the early 1980’s, then second-generation (2G) digital systems came in the
early 1990’s. Now third-generation (3G) systems are slowly unfolding all over the world
while intensive conceptual and research work toward the definition of a future system has
been already started.
2G systems, such as GSM and IS-95, were essentially designed for voice and low data rate
applications. In an effort to address customer demands for high-speed data communication,
telecommunication companies have been launching 3G systems where the business focus has
shifted from voice services to multimedia communication applications over the Internet. Despite
the increasing penetration rate of 3G systems in the wireless market, 3G networks are
challenged primarily in meeting the requirements imposed by the ever-increasing demands of
high-throughput multimedia and internet applications. Additionally, 3G systems consist primarily
of wide area networks and thus fall short of supporting heterogeneous networks, including
wireless local area networks (LANs) and wireless personal area networks (WPANs).
Diversity Techniques for Fading Channels
The characteristics of wireless channel impose fundamental limitations on the performance
of wireless communication systems. The wireless channel can be investigated by composing
it into two parts, i.e., large-scale (long-term) impairments including path loss, shadowing
and small-scale (short-term) impairment which is commonly referred as fading. The
former component is used to predict the average signal power at the receiver side and the
transmission coverage area. The latter is due to the multipath propagation which causes random
fluctuations in the received signal level and affects the instantaneous signal-to-noise ratio
(SNR).
For a typical mobile wireless channel in urban areas where there is no line of sight propagation
and the number of scatters is considerably large, the application of central limit theory
indicates that the complex fading channel coefficient has two quadrature components which
are zero-mean Gaussian random processes. As a result, the amplitude of the fading envelope
follows a Rayleigh distribution. In terms of error rate performance.
Time Diversity
In this form of diversity, the same signal is transmitted in different time slots separated by
an interval longer than the coherence time of the channel. Channel coding in conjunction
with interleaving is an efficient technique to provide time diversity. In fast fading environments
where the mobility is high, time diversity becomes very efficient. However, for slowfading
channel (e.g., low mobility environments, fixed-wireless applications), it offers little
protection unless significant interleaving delays can be tolerated.
Frequency Diversity
In this form of diversity, the same signal is sent over different frequency carriers, whose
separation must be larger than the coherence bandwidth of the channel to ensure independence
among diversity channels. Since multiple frequencies are needed, this is generally not a
bandwidth-efficient solution. A natural way of frequency diversity, which is sometimes referred
to as path diversity, arises for frequency-selective channels. When the multipath delay
spread is a significant fraction of the symbol period, the received signal can be interpreted as a linear combination of the transmitted signal weighted by independent fading coefficients.
Therefore, path diversity is obtained by resolving the multipath components at different delays
using a RAKE correlator [2], which is the optimum receiver in the MMSE sense designed
for this type of channels.
Space Diversity
In this form of diversity, which is also sometimes called as antenna diversity, the receiver
and/or transmitter uses multiple antennas. This technique is especially attractive since it does
not require extra bandwidth. To extract full diversity advantages, the spacing between antenna
elements should be wide enough with respect to the carrier wavelength. The required
antenna separation depends on the local scattering environment as well as on the carrier frequency.
For a mobile station which is near the ground with many scatters around, the channel
decorrelates over shorter distances, and typical antenna separation of half to one carrier
wavelength is sufficient. For base stations on high towers, a larger antenna separation of several
to tens of wavelengths may be required.
Diversity Combining Techniques
There exist different combining techniques, each of which can be used in conjunction
with any of the aforemetioned diversity forms. The most common diversity combining techniques
are selection, equal gain and maximal ratio combining [2]. Selection combining (SC)
is conceptually the simplest; it consists of selecting at each time, among the available diversity
branches (channels), the one with the largest value of SNR. Since it requires only a
measure of the powers received from each branch and a switch to choose among the
branches, it is relatively easy to implement. However, the fact that it disregards the information
obtained from all branches except the selected one indicates its non-optimality. In equal
gain combining (EGC).
[attachment=30555]
Abstract
The revolutionary concept of space-time coding introduced in the last decade has
demonstrated that the deployment of multiple antennas at the transmitter allows for
simultaneous increase in throughput and reliability because of the additional degrees of
freedom offered by the spatial dimension of the wireless channel. However, the use of
antenna arrays is not practical for deployment in some practical scenarios, e.g., sensor
networks, due to space and power limitations.
A new form of realizing transmit diversity has been recently introduced under the name of
user cooperation or cooperative diversity. The basic idea behind cooperative diversity rests
on the observation that in a wireless environment, the signal transmitted by the source node is
overheard by other nodes, which can be defined as “partners” or “relays”. The source and its
partners can jointly process and transmit their information, creating a “virtual antenna array”
and therefore emulating transmit diversity.
Most of the ongoing research efforts in cooperative diversity assume frequency flat
channels with perfect channel knowledge. However, in practical scenarios, e.g. broadband
wireless networks, these assumptions do not apply. Frequency-selective fading and imperfect
channel knowledge should be considered as a more realistic channel model. The
development of equalization and channel estimation algorithms play a crucial element in the
design of digital receivers as their accuracy determine the overall performance.
Introduction
A quick glimpse of recent technological history reveals out that mobile communication
systems create a new generation roughly every 10 years. First-generation analogue systems
were introduced in the early 1980’s, then second-generation (2G) digital systems came in the
early 1990’s. Now third-generation (3G) systems are slowly unfolding all over the world
while intensive conceptual and research work toward the definition of a future system has
been already started.
2G systems, such as GSM and IS-95, were essentially designed for voice and low data rate
applications. In an effort to address customer demands for high-speed data communication,
telecommunication companies have been launching 3G systems where the business focus has
shifted from voice services to multimedia communication applications over the Internet. Despite
the increasing penetration rate of 3G systems in the wireless market, 3G networks are
challenged primarily in meeting the requirements imposed by the ever-increasing demands of
high-throughput multimedia and internet applications. Additionally, 3G systems consist primarily
of wide area networks and thus fall short of supporting heterogeneous networks, including
wireless local area networks (LANs) and wireless personal area networks (WPANs).
Diversity Techniques for Fading Channels
The characteristics of wireless channel impose fundamental limitations on the performance
of wireless communication systems. The wireless channel can be investigated by composing
it into two parts, i.e., large-scale (long-term) impairments including path loss, shadowing
and small-scale (short-term) impairment which is commonly referred as fading. The
former component is used to predict the average signal power at the receiver side and the
transmission coverage area. The latter is due to the multipath propagation which causes random
fluctuations in the received signal level and affects the instantaneous signal-to-noise ratio
(SNR).
For a typical mobile wireless channel in urban areas where there is no line of sight propagation
and the number of scatters is considerably large, the application of central limit theory
indicates that the complex fading channel coefficient has two quadrature components which
are zero-mean Gaussian random processes. As a result, the amplitude of the fading envelope
follows a Rayleigh distribution. In terms of error rate performance.
Time Diversity
In this form of diversity, the same signal is transmitted in different time slots separated by
an interval longer than the coherence time of the channel. Channel coding in conjunction
with interleaving is an efficient technique to provide time diversity. In fast fading environments
where the mobility is high, time diversity becomes very efficient. However, for slowfading
channel (e.g., low mobility environments, fixed-wireless applications), it offers little
protection unless significant interleaving delays can be tolerated.
Frequency Diversity
In this form of diversity, the same signal is sent over different frequency carriers, whose
separation must be larger than the coherence bandwidth of the channel to ensure independence
among diversity channels. Since multiple frequencies are needed, this is generally not a
bandwidth-efficient solution. A natural way of frequency diversity, which is sometimes referred
to as path diversity, arises for frequency-selective channels. When the multipath delay
spread is a significant fraction of the symbol period, the received signal can be interpreted as a linear combination of the transmitted signal weighted by independent fading coefficients.
Therefore, path diversity is obtained by resolving the multipath components at different delays
using a RAKE correlator [2], which is the optimum receiver in the MMSE sense designed
for this type of channels.
Space Diversity
In this form of diversity, which is also sometimes called as antenna diversity, the receiver
and/or transmitter uses multiple antennas. This technique is especially attractive since it does
not require extra bandwidth. To extract full diversity advantages, the spacing between antenna
elements should be wide enough with respect to the carrier wavelength. The required
antenna separation depends on the local scattering environment as well as on the carrier frequency.
For a mobile station which is near the ground with many scatters around, the channel
decorrelates over shorter distances, and typical antenna separation of half to one carrier
wavelength is sufficient. For base stations on high towers, a larger antenna separation of several
to tens of wavelengths may be required.
Diversity Combining Techniques
There exist different combining techniques, each of which can be used in conjunction
with any of the aforemetioned diversity forms. The most common diversity combining techniques
are selection, equal gain and maximal ratio combining [2]. Selection combining (SC)
is conceptually the simplest; it consists of selecting at each time, among the available diversity
branches (channels), the one with the largest value of SNR. Since it requires only a
measure of the powers received from each branch and a switch to choose among the
branches, it is relatively easy to implement. However, the fact that it disregards the information
obtained from all branches except the selected one indicates its non-optimality. In equal
gain combining (EGC).