RECENT ADVANCES
IN WIRELESS
COMMUNICATIONS
AND NETWORKS

Edited by Jia-Chin Lin

Recent Advances in Wireless Communications and Networks
Edited by Jia-Chin Lin Published by InTech
Janeza Trdine 9, 51000 Rijeka, Croatia

Copyright © 2011 InTech
All chapters are Open Access articles distributed under the Creative Commons
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work is properly cited. After this work has been published by InTech, authors Contents

Preface IX
Part 1 Physcial and MAC Layers 1
Chapter 1 A Study of Cramér-Rao-Like Bounds and
Their Applications to Wireless Communications 3
Kao-Peng Chou and Jia-Chin Lin
Chapter 2 Synchronization for OFDM-Based Systems 23
Yu-Ting Sun and Jia-Chin Lin
Chapter 3 ICI Reduction Methods in OFDM Systems 41
Nadieh M. Moghaddam and Mohammad Mohebbi
Chapter 4 Multiple Antenna Techniques 59
Han-Kui Chang, Meng-Lin Ku, Li-Wen Huang, and Jia-Chin Lin
Chapter 5 Diversity Management in MIMO-OFDM Systems 95
Felip Riera-Palou and Guillem Femenias
Chapter 6 Optimal Resource Allocation in OFDMA
Broadcast Channels Using Dynamic Programming 117
Jesús Pérez, Javier Vía and Alfredo Nazábal
Chapter 7 Primary User Detection in
Multi-Antenna Cognitive Radio 139
Oscar Filio, Serguei Primak and Valeri Kontorovich
Chapter 8 Multi-Cell Cooperation for Future Wireless Systems 165
A. Silva, R. Holakouei and A. Gameiro
Part 2 Upper Layers 187
Chapter 9 Joint Call Admission Control in
Integrated Wireless LAN and 3G Cellular Networks 189

Chapter 19 Wireless Sensor Networks in
Smart Structural Technologies 405
Yang Wang and Kincho H. Law
Chapter 20 Extending Applications of Dielectric Elastomer
Artificial Muscles to Wireless Communication Systems 435
Seiki Chiba and Mikio Waki
Preface

Many exciting impacts on our daily life are shortly anticipated due to recent advances
in wireless communication networks that enable real-time multimedia services to be
provided via mobile broadband Internet on a wide variety of terminal devices. The
trend is mainly driven by the evolution of wireless networks and advanced wireless
information and communication technology (ICT). The progression to fourth-
generation (4G) or IMT-advanced systems is expected to significantly change usage
habits and introduce new services, such as the services supported by higher spectral-
efficiency communication technology and self-configurable, high-feasibility networks.
The Third Generation Partnership Project (3GPP) Long-Term Evolution (LTE)
continues to be enhanced as this book is being written.

hottest issues and concluding with a comprehensive discussion. The content in each
chapter is taken from many publications in prestigious journals and followed by
fruitful insights. The chapters in this book also provide many references for relevant
topics, and interested readers will find these references helpful when they explore
these topics further. These twenty chapters are arranged in order from the lowest layer
to the upper layers of wireless communication. This book was naturally partitioned
into 3 main parts. Part A consists of eight chapters that are devoted to physical layer
(PHY) and medium access control (MAC) layer research. Part B consists of five
chapters that are devoted to upper layer research. Finally, Part C consists of seven
chapters that are devoted to applications and realizations.
Chapter 1 is an introduction to topics at an inner receiver in wireless communications,
including a historical perspective and a description of Cramér-Rao-like bounds and
relevant applications to estimation techniques in wireless communications. Chapter 2
conducts a thorough review of the initial synchronization techniques applied to
wireless orthogonal-frequency-division-multiplexing (OFDM) communications.
Chapter 3 is devoted to deeply investigating novel techniques of inter-carrier
interference (ICI) reduction in practical OFDM communications. Chapter 4 is focused
on multiple-antenna techniques from diversity, spatial multiplexing to beamforming
techniques. Chapter 5 deeply investigates diversity management techniques in MIMO-
OFDM communication systems. Chapter 6 thoroughly investigates resource allocation
methods in OFDMA broadcast channels. This is the downlink scenario in either LTE-A
or IEEE 802.16m wireless communications. Chapter 7 is dedicated to primary user
detection in multi-antenna environments. Spectral sensing techniques are considered
as the most important issue in recent research regarding cognitive radios (CRs) or
cognitive networks. Chapter 8 focuses on multi-cell cooperation methodology. In third
generation mobile communications, macro-diversity was investigated. In similar
environments, multi-cell cooperation may be expected in opportunistic communication
networks.
Chapter 9 covers a novel technique on joint call admission control in integrated
wireless local area networks (WLAN) and cellular networks. Chapter 10 is devoted to

Physcial and MAC Layers

1
A Study of Cramér-Rao-Like Bounds and
Their Applications to Wireless Communications
Kao-Peng Chou and Jia-Chin Lin
Department of Communication Engineering, National Central University
Jhongli, Taoyuan,
Taiwan
1. Introduction
Estimation theory has been developed over centuries. There are several approaches to
utilizing this theory; in this chapter, these approaches are classified into three types. Type I
includes the oldest two methods, the least squares (LS) and moment methods; both of these
methods are non-optimal estimators. The least squares method was introduced by Carl
Friedrich Gauss. Least squares problems fall into linear and non-linear categories. The linear
least squares problem is also known as regression analysis in statistics, which have a closed
form solution. An important feature of the least squares method is that no probabilistic
assumptions of the data are made. Therefore, the linear least squares approach is used for
parameter estimation, especially for low complexity design (Lin, 2008; 2009). The design
goal of the least squares estimator is to find a linear function of observations whose
expectation is a linear function of the unknown parameter with minimum variance. In
addition, the least squares method corresponds to the maximum likelihood (ML) criterion if
the experimental errors are normally distributed and can also be derived from the moment
estimation. As an alternative to the LS method, the moment method is another simple
parameter estimation method with probabilistic assumptions of the data. The general
moment method was introduced by K. Pearson. The main procedure in the moment method
involves equating the unknown parameter to a moment of distribution, then replacing the
moment with a sample moment to obtain the moment estimator. Although the moment
estimator has no optimal properties, the accuracy can be validated through lengthy data
measurements. This is mainly because the estimator based on moment can be maintained to

Fisher with the concepts of consistency, efficiency and sufficiency of the estimation function.
The ML estimator is required when MVUE does not exist or cannot be found. An advantage
of the ML estimator is that a practical estimation is easy to obtain through the prescribed
procedures. Another advantage of this approach is that MVUE can be approximated due to
its efficiency. Thus, from the theoretical and practical perspectives, the ML approach is the
most important and widely used estimation method of this century (Lin, 2003).
Because the ML estimator is essential in estimation theory, the analysis of its performance is
a benchmark of estimator design. This benchmark is commonly known as the Cramer-Rao
lower bound (CRLB), which is named after Harald Cramer and Calyampudi Radhakrishna
Rao. In section 2, the definition of the CRLB is introduced with several examples. A general
case of CRLB under two common communication channels is then introduced in section 3.
To establish basic knowledge of hybrid parameter estimation, random parameter estimation
is presented in section 4. In section 5, Cramer-Rao-like bounds for hybrid parameter
estimation are introduced and compared with each other. Lastly, we summarize some
practical cases and compare these cases with modified CRB which is most common used
Cramer-Rao-like bounds.
2. Cramer-Rao lower bound (CRLB)
The Cramer-Rao lower bound (CRLB) is a lower bound on the variance of any unbiased
estimator. Many other variance bounds exist, but the CRLB is the easiest one to derive and is
thus widely used in many estimation studies. This theory provides a benchmark for
examining the performance of novel estimation algorithms and also highlights the
impossibility of finding an unbiased estimator with a variance less than this lower bound.
Before introducing the definition of CRLB, there is a simple estimation example that may
could help promote understanding of the basic CRLB concept.
Example 2.1
There is a simple signal transmission model with a transmitted signal
s
, a received signal
[]rn
and an additive white Gaussian noise []wn .

p
rn s rn s
σ
πσ
⎡
⎤
=−−
⎢
⎥
⎣
⎦
(2)

Substituting the estimator we chose in this likelihood function yields ()
2
2
2
11
ˆˆ
(;) exp
2
2
p
ss s s
σ
πσ
⎡

⎜⎟
⎜⎟
∂
⎝⎠
. (4)

Furthermore, we are interested in finding a more accurate estimator by lowering the
variance
2
σ
. This can be achieved by exploiting multiple observations. Assuming the
observation samples are identical independently distributed, the likelihood function for
multiple observations is

()
()
2
2
2
2
1
11
([ ];) exp [ ]
2
1
2
N
N
p
ns rn s

=
, (6)
which is an unbiased estimator, namely
{
}
ˆ
Es s
=
. We can also find the estimation variance
using equation (4); the result is similar to the single observation MLs with a factor N in the
denominator:

2
ˆ
var( )s
N
σ
= . (7)
An extreme case occurs when N approaches
∞
, and the process reduces the estimation
variance to 0. From this simple example, we can summarize that the ultimate goal of
estimator design is to find the minimum variance unbiased estimator (MVUE), and if we
wish to illustrate the performance of our estimator, then estimation variance can be found
through the likelihood function. Now, we are ready to define the CRLB (Kay, 1998).

Recent Advances in Wireless Communications and Networks

6
<Theorem>

2
;
2
1
ˆ
var( )
ln ( ; )
r
pr
E
θ
θ
θ
θ
≥
⎡
⎤
∂
−
⎢
⎥
∂
⎢
⎥
⎣
⎦
. (9)
An unbiased estimator may be found that attains the bound for all
θ
if and only if

()
2
;
2
ln ( ; )
r
pr
IE
θ
θ
θ
θ
⎡
⎤
∂
=−
⎢
⎥
∂
⎢
⎥
⎣
⎦
, (11)
which is used to calculate the covariance matrices associated with maximum-likelihood estimates.
An unbiased estimator that achieves the variance lower bound is referred to as “efficient”.
In other words, an unbiased estimator that achieves the CRLB is an efficient estimator and
must be MVUE. Figures 1 and 2 are illustrations of the relationship between a MVU
estimator and the CRLB.


= , but
a biased estimator may outperform than an unbiased one. For example, in some situations,
the relationship between a MVUE and a Bayesian MSE estimator may be illustrated in figure
3. Fig. 3. MVUE vs. Bayesian estimator

Recent Advances in Wireless Communications and Networks

8
In this example, the Bayesian MSE estimator is an unbiased estimator. The performance
comparison in figure 3 shows that within a certain parameter interval, the biased Bayesian
estimator may have lower estimation variance than MVUE’s. However, this comparison also
shows that the biased estimator performs terribly outside this interval. Thus, the unbiased
estimator has an advantage in terms of consistent performance.
2.1 Asymptotic CRLB
For some cases in which the closed form of the CRLB may not be derived, the asymptotic
CRLB can be used instead; this form can be attained by assuming that infinite observation
samples are available. Under this assumption, we have an observation sample with an
infinite signal-to-noise ratio (SNR).
3. General case CRLB
3.1 Gaussian noise
The AWGN channel is the most common channel model in wireless communication, which
was also used in the example in the last section. In example 2.1, we only consider the
estimate of symbol s . Now, a general form of any parameter
θ
is derived.
Example 3.1
Assuming symbol s is transmitted with a general unknown parameter

2
N
n
n
N
n
pr t st r t st
θθ
σ
πσ
−
=
⎡
⎤
=−−
∑
⎢
⎥
⎣
⎦
(13)

[]
1
2
0
ln ( ( ); ( ), )
1(;)
() (; )
N

ln ( ( ); ( ), )
1(;) (;)
() (; )
N
n
n
n
pr t st
st st
rt st
θ
θ
θ
θ
θ
θσ θ
−
=
∂
∂∂
⎛⎞
=+−
∑
⎜⎟
∂
∂∂
⎝⎠
(15)
Taking the expectation of
2

n
pr t st
st
IE
θ
θ
θ
θ
θ
θσ
−
=
⎧⎫
∂
∂
⎪⎪
⎛⎞
=−
∑
⎨⎬
⎜⎟
∂
∂
⎝⎠
⎪⎪
⎩⎭
(16)
Finally, the inverse recipocal of the Fisher‘s information produced by the CRLB in the
AWGN channel.


⎝⎠
(17)

3.2 Complex Gaussian channel
Another commonly seen channel is complex Gaussian channel. The mobile communication
and wireless communication usually introduce the Rayleigh fading due to multipath delay
spread and Doppler shift. In numerical simulation we may use the Jake’s (Clarke) model,
but in theoretical analysis, complex Gaussian channel is more popular, because it has
Rayleigh distributed amplitude with an uniformly distributed phase, which is convenient to
use and without loss of generality.
Example 3.2
The signal model can be extended from the general AWGN channel model. We multiply the
Rayleigh distributed channel gain
0
α
and the uniformly distributed channel phase
0
j
e
φ
−

with the symbol
(; )
u
st
θ
.
j
st w t
α
αθ
=
++ (19)

Because the
I
α
,
Q
α
and ()
n
wt terms are Gaussian distributed, the received signal ()
n
rt is
also Gaussian distributed. To find the joint likelihood function, the mean m
r
and variance
2
r
σ
of the received signal should be derived.

(1 ) ( ; )
rA
mjst
η

r
r
r
r
rt m
prtst
θ
σ
πσ
−
=−
(22)

4. Random parameter estimation
In previous sections, some basic knowledge of estimation bounds were introduced based on
unknown parameters with random interference. These kinds of estimation problems are
categorized in the classical estimation approach. Some properties of estimation methods are
listed in Table 1.

Recent Advances in Wireless Communications and Networks

10
Parameter
types
Sample
distribution
Parameter
distribution
LS Unknown Unknown Non
Moment Unknown Known Non

n
n
p
rt s r t s
πσ
−
=
⎛⎞
=−−
∑
⎜⎟
⎝⎠
(24)
Using Bayes’ rule,

(;())(())
(();)
()
p
srt
p
rt
prt s
ps
= (25)
After certain computations, the conditional pdf with a posteriori information is obtained as

2
;
2

σ
σ
σ
=
+
; (27)

2
;;
22
s
sr sr
s
N
x
μ
μ
σ
σσ
⎛⎞
=+
⎜⎟
⎜⎟
⎝⎠
. (28)
The MMSE estimator is then determined as

;
ˆ
{|()} (1 )

22 2
2
2
2
ˆˆ
() [ ]
s
s
Bmse s E s s
NN
N
σ
σσ
σ
σ
⎛⎞
⎜⎟
=−= ≤
⎜⎟
+
⎜⎟
⎝⎠
(31)

As
2
s
σ
→∞ i.e., without any information from a prior knowledge, the bound would be the
same with the sample mean estimator. This result can be compared with that of the first

r
θ is treated as a nuisance parameter, which means that these random
parameter are undesired.
Example 5.1
Reformulating the signal model and likelihood function yields

() (; ) ()
nn
rt st wt
=
+θ (33)

[]
2
2
2
() (; )
1
( ( ), ; ( ), ) exp( )
2
2
n
nr u
rt st
pr t st
σ
πσ
−
=−
θ

1(,)
() (, )
N
nu
u
nu
n
uu
p
rtst
st
rt st
σ
−
=
∂
∂
=−
∑
∂∂
θ
θ
θ
θθ
(36)
2.
[]
2
2
2

θ
θ
θθ
(37)
3.
()
))
,
ln ( ( ); ( , ) ln ( ( ); ( , )
nu nu
r
ij
ij
pr t st pr t st
IE
θθ
⎧⎫
∂∂
⎪⎪
=
⎨⎬
∂∂
⎪⎪
⎩⎭
θθ
θ
(38)
4.
()
,

2
() (;)
1
((), ;(), ) exp( )
2
2
rtst
n
pr t st
nr u
σ
πσ
−
=−
θ
θθ
(41)

[]
1
2
0
ln ( ( ), ; ( ), )
1(;)
() (; )
N
n
pr t st
st
nr u

st st
rtst
n
σ
−
=
∂
∂∂
⎛⎞
=+−
∑
⎜⎟
∂
∂∂
⎝⎠
θθ
θθ
θ
θ
θθ
(43)
Because the joint pdf is considered, the expection of Fisher’s information should be taken
with respect to ( ( ), )prt
r
θ()
,
,

We define the information matrix with respect to the conditional pdf ( ( ); )prt
r
θ
as

A Study of Cramér-Rao-Like Bounds and Their Applications to Wireless Communications

13

()
0
;
,
ln ( ( ), ; ( ), ) ln ( ( ), ; ( ), )
|
nr u nr u
r
r
ij
r
ij
pr t st pr t st
IE
θθ
⎧
⎫
∂∂
⎪
⎪
=

ij
r
ij
pr t st pr t st
IEE
EII
θθ
⎧
⎫
⎧
⎫
∂∂
⎪
⎪⎪⎪
=
⎨
⎨⎬⎬
∂∂
⎪
⎪
⎪
⎪
⎩⎭
⎩⎭
=
θ
θ
θ
θ
θθ θθ

information is no longer in a matrix form, and derivation is easier.

()
,
1
ˆˆ
() var()
ii
ii
MCRB
I
θ
θ
⎡⎤
⎢⎥=≤
⎢⎥
⎣
⎦
θ
(48)
An previously reported example can help distinguish the difference between these CR-like
bounds (F. Gini, 2000).
Example 5.2
When considering a data-aided joint frequency offset estimation case, the signal model can
be described as

2
() () ()
D
jft

αα
=θ
can be defined. Because this is a data-aided case, ()st can be a pilot or
preamble, and we can assume that
*
()() 1
stst
=
without loss of generality. Then the signal
after pilot removal is

2
*
() ()()
() ()
D
n
nn
jft
IQ n
xt rtst
je vtx
π
αα
=
=+ +
(50)