Study and Measurement of Scatter Effect on Wireless

EXAMENSARBETE I TRÅDLÖSA SYSTEM 120 HP, AVANCERAD NIVÅ
STOCKHOLM, SVERIGE 2015
Study and Measurement of
Scatter Effect on Wireless
Channel using Realtime LTE
Co-Operative Relays
UMER ZEESHAN ASHRAF
KTH KUNGLIGA TEKNISKA HÖGSKOLAN
SKOLAN FÖR INFORMATIONS- OCH KOMMUNIKATIONSTEKNIK
Abstract
Relays are an important application of the fixed wireless access channel (FWA), with
fast growing needs for higher throughput cooperative schemes are standardized to
enhance throughput and cooperative relays are a realization of the standard. We
study the effects of moving scatters on the FWA channel with simulation and real-time
LTE measurements with testbed and characterize the FWA MIMO channel based on
its performance and small scale movements in the environment, compare the LOS and
NLOS effects of channel in relation to scatter movements and antenna height. The
measurement and simulation results show that there is significant effect of scatters on
the characteristics of wireless channel. The doppler spectrum bandwidth increases in
relation to the both speed and number of moving scatters. On performance metric
MIMO capacity is studied and shown that capacity increases with antenna height
in NLOS condition and presence of scatters improve the channel capacity in LOS
condition.
i
Dedication
All the praise and thanks be for the lord Almighty. I would like to dedicate this work
to the excellent teachers who delivered the knowledge. Thanks to my loving wife
without whom this would not have been possible. Thanks to my parents for their
eternal love and prayers, my sister and friends for supporting me.
ii
Acknowledgements
I would like to thanks to prof. Ben for accepting my work, for the continuous feedback
and suggestions, Michael for being a mentor and a great person, my guide and Thesis
advisor, without him this would not have been possible. Vodafone chair for giving
me the opportunity to do a research study, it was a very important step in my life.
iii
Table of Contents
List of Acronyms
v
List of Tables
vi
List of Figures
vii
1 Introduction
1.1 The Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . .
1
2
2 Wireless channel
2.1 Background . . . . . . . . . . . . . . . . . . . . .
2.1.1 Multi-path Fading Channels . . . . . . . .
2.1.2 Time variance of the channel . . . . . . . .
2.2 State of the art . . . . . . . . . . . . . . . . . . .
2.2.1 Ricean K-Factor . . . . . . . . . . . . . .
2.2.2 K-Factor Estimation . . . . . . . . . . . .
2.2.3 Doppler Spectrum model for FWA channel
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4 Simulation
4.1 OFDM transceiver chain . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Doppler spectrum fixed wireless channel . . . . . . . . . . . . . . . .
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5 Measurements and Field trials
5.1 Test-bed equipment . . . . . .
5.2 Indoor measurements . . . . .
5.3 Outdoor measurements . . . .
5.3.1 Evaluation chain . . .
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3 Methodology
3.1 Channel model . . . . . . . . . . . . . . . . . .
3.1.1 OFDM channel estimation . . . . . . . .
3.2 Performance . . . . . . . . . . . . . . . . . . . .
3.2.1 Signal and noise power estimation . . . .
3.2.2 Subcarrier power variance . . . . . . . .
3.2.3 Capacity . . . . . . . . . . . . . . . . . .
3.3 Effect of Imperfect CSI on channel performance
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6 Results
6.1 Indoor measurements . .
6.1.1 Sub-carrier power
6.2 Outdoor measurements .
6.2.1 Sub-carrier power
6.3 Capacity . . . . . . . . .
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7 Conclusions
7.1 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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variance
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variance
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A SORBAS test equipment
A.1 AVAILABLE SORBAS TEST UE OPTIONS . . . . . . . . .
A.2 DUPLEX MODES . . . . . . . . . . . . . . . . . . . . . . . .
A.3 RADIO FREQUENCY . . . . . . . . . . . . . . . . . . . . . .
A.4 PHYSICAL LAYER FEATURES . . . . . . . . . . . . . . . .
A.4.1 Downlink Characteristics . . . . . . . . . . . . . . . . .
A.4.2 Uplink Characteristics . . . . . . . . . . . . . . . . . .
A.4.3 PhysicalSignals and Channels . . . . . . . . . . . . . .
A.5 LAYER 2 FEATURES . . . . . . . . . . . . . . . . . . . . . .
A.5.1 The UE MAC and RLC implementation according to
Release 8 . . . . . . . . . . . . . . . . . . . . . . . . .
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3GPP
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v
List of Acronyms
LTE Long term evolution
COMP Co-ordinated multipoint
MIMO Multiple Input Multiple Output
CSI Channel state information
CoR Cooperative relay
UE User equipment
AWGN Additive white Gaussian noise
NLOS Non line-of-sight
PDF Probability density function
WSS Wide sense stationary
ACF Autocorrelation function
CTF Channel transfer function
CIR Channel impulse response
BER Bit error rate
SNR Signal to noise ratio
KPI key parameter indications
RSRP Reference signal received power
RSRQ Reference signal received quality
vi
List of Tables
2.1
Coherence time Tc [sec] . . . . . . . . . . . . . . . . . . . . . . . . . .
5
4.1
4.2
OFDM Simulation parameters . . . . . . . . . . . . . . . . . . . . . .
Scatter simulation parameters . . . . . . . . . . . . . . . . . . . . . .
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5.1
5.2
Indoor Measurement parameters . . . . . . . . . . . . . . . . . . . . .
Outdoor Measurement parameters . . . . . . . . . . . . . . . . . . . .
32
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vii
List of Figures
1.1
Cooperative relay scenario . . . . . . . . . . . . . . . . . . . . . . . .
2
2.1
2.2
2.3
2.4
Multipath fading . . . . . . . . . . .
Jakes Spectrum and its assumption. .
K-factor measurement results. . . . .
Doppler spectrum for FWA channel.
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3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
OFDM transmitter . . . . . . . . . .
OFDM receiver . . . . . . . . . . . .
Reference symbols grid for 1 antenna
Parallel narrowband subchannels . .
OFDM channel estimation structure
CTF and CIR . . . . . . . . . . . . .
Channel Capacity . . . . . . . . . . .
Antipodal signaling constellation . .
Performance with Imperfect CSI . . .
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4.1
4.2
4.3
4.4
4.5
4.6
4.7
OFDM baseband transmitter . .
Baseband signal through channel
OFDM baseband receiver . . . . .
ML detector . . . . . . . . . . . .
Symbol error rate curves . . . . .
Scatter simulation model . . . . .
Doppler spectrum analysis . . . .
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5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
SORBAS test equipment . . . . . . . . . .
Indoor measurement environment . . . . .
Lab dimensions and movement description
Indoor Static environment . . . . . . . . .
Indoor 1 person moving environment . . .
Indoor 2 persons moving environment . . .
Indoor 4 persons moving environment . . .
Test-bed map . . . . . . . . . . . . . . . .
Measurement location and Van . . . . . .
Measurement location and Van . . . . . .
Post dump analysis chain . . . . . . . . . .
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6.1
6.2
6.3
Indoor environment SC power variance . . . . . . . . . . . . . . . . .
Outdoor environment SC power variance . . . . . . . . . . . . . . . .
Measured MIMO capacity . . . . . . . . . . . . . . . . . . . . . . . .
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1
Chapter 1
Introduction
Cellular networks are widely used means of communication and hold importance in
business and daily life matters. The use of mobile phones has been increased exponentially in the last decade because of advances in wireless networks and affordable
packages for the end user. A recent survey reveals the number of mobile phone users
worldwide has reached to 5.6 billion[1]. The demand for higher rates in cellular networks is an ever increasing need. There has been an exponential rise in the cellular
networks traffic mainly because of the multimedia, streaming applications and high
speed internet access. We have reached up to 1 Gbps of peak data rate in recent Long
term evolution(LTE) advanced standards and still the need grows bigger. A Global
mobile traffic forecast done by Cisco predicts an exponential increase from 2011 to
2016[2]. New technologies are emerging with efficient bandwidth utilization, scheduling and multiple access schemes. The main challenge in this ever increasing demand
for higher traffic requirements is the very nature of wireless channel. Wireless channel
changes to a significant extent with small changes in position and random for a mobile
user. Another challenge is the limited bandwidth, which is very expensive and must
be used efficiently and reused as frequently as possible. The wireless networks are
becoming denser and denser and the systems are becoming interference limited. Coordinated multipoint(COMP) is a technology which offers solution to the interference
problem with cooperation between neighbouring base stations. There is a back-haul
link on which channel state information (CSI) and scheduling information is shared
between the base stations. With help of a pre-coder which uses channel information
to effectively neutralize the channel attenuation in advance and joint processing (JP)
which enables the multiplexed signals to be processed jointly and as a result the system becomes a multi input multi output(MIMO) system with transmitter antennas
spatially distributed. This offers a likelihood that MIMO channels would be uncorrelated and independent with a good overall MIMO capacity. An important application
of COMP is that it can be used for data rate enhancement at cell edge because the
user at the ell edge usually faces interference and has a bad channel condition. The
CSI feedback and JP poses a delay in case of a mobile user is a problem because the
channel information becomes outdated and the pre-coder can not function. Another
challenge is optimum clustering and user grouping of mobile users. The scenario in
which receiver is fixed and a useful application is a relay case, the situation is much
simpler. Figure 1.1 describes the scenario. In case of a relay we face a fixed wireless
access(FWA) channel which offers an interesting opportunity for our use case as it is
expected not to vary too much with time. The only causes of variation in the CSI
would be the movement in environment. The focus of this thesis is to study these
variations in environment and their effect on channel. Another prospect is the issue
2
regarding deployment of the relays. The height and location are important parameters in this regard. An analysis of the Cooperative relay(CoR) channel based on
height is performed in an outdoor scenario along the road with cars passing by as
moving scatterers.
Figure 1.1: Cooperative relay scenario
1.1
The Problem Statement
For this thesis work we study three main issues regarding cooperative relays
1. How the scatterers in environment effect channel stability of FWA channel
2. How the channel behaves with deterministic movements in environment
3. What is the performance of the MIMO CoR channel with increasing height at
different locations
3
Chapter 2
Wireless channel
2.1
2.1.1
Background
Multi-path Fading Channels
The physical medium through which the information signal travels, from transmitter
to the receiver is called communication channel. For instance, telephone line in wired
system and environment between the transmitter and the receiver in wireless system is
called as communication channel. Wireless channels are highly unreliable in nature,
both in time and frequency domain. The distortion experienced by the received
signal after propagating through wireless channel is called as fading. In multi-path
propagation, the transmitted signal can take several paths to reach the receiver. The
signals reaching through different paths experience different delays. The superposition
of these signals at the receiver can be constructive or destructive depending upon the
delays introduced by each path. This causes variation in received signal strength,
frequency and phase at the receiver and thus give rise to multi-path fading. Figure
1.2 describes a multipath fading wireless channel. The multi-path fading can be
further divided into two types, i.e. large scale fading and small scale fading.
Figure 2.1: Multipath fading
Large scale fading represents the variation in the mean of received signal power
over the large distance as compare to the wavelength of the signal. It occurs when
receiver is shadowed by large terrain obstacles such as hills or buildings. Large scale
fading is often expressed in terms of mean path loss and log-normal distributed variation about the mean for a given distance between transmitter and the receiver [3].
Log-normal distribution means that the logarithm of signal power follows norm distribution.
Small scale fading includes the variation in received signal power over small distances, comparable or less than wavelength of the signal. This occurs due to scatters
present in the communication channel. These scatters are created due to reflection,
4
diffraction and scattering of radio waves from different objects and surfaces located
between the transmitter and the receiver. The received signal is the summation of all
these scattered signals. This causes fluctuations in the received signal strength. Let
us assume that the bandpass signal s(t)is transmitted during an interval 0 ≤ t ≤ T .
It can be expressed in terms of its baseband waveform s l (t) as follows
<{sl (t)ej2πfc t }
(2.1)
Where <{.} denotes the real part of {.} and fc is the carrier frequency. While
travelling through multi-path fading environments s(t) will be modified by a complex
factor α(t) that takes care of both large and small scale fading. Assuming that there
are i multiple scattering paths, each having time variant propagation delay τi (t) and
a time variant multiplicative factor αi , the received bandpass signal can be written
as follows according to [4]
X
y(t) =
αi s[t − τi (t)] + n(t)
(2.2)
i
"
y(t) = <
#
X
|αi (t)|ejθi (t) sl (t − τi (t)) ej2πfc t
!
+ n(t)
(2.3)
i
Where |αi (t)| is the time varying amplitude and θi (t) is the time varying phase
of the complex gain factor αi , and n(t) at any instant t denotes additive white Gaussian noise (AWGN) at the receiver with zero mean and known variance. Main source
of AWGN is thermal noise causing excitation in electrons or because of interference
collected by the antenna. As n(t) is the sum of large number of statistically independent random variables hence according to the central limit theorem [3], it can be
modelled as Gaussian random variable. Assuming perfect carrier recovery and timing
synchronization, the baseband received signal can be modelled as
X
yl (t) =
|αi (t)|ejθi (t) sl (t − τi (t)) + n(t)
(2.4)
i
Since y l (t) is the response of an equivalent baseband channel to an equivalent baseband signal s l (t), the expression for time variant baseband channel impulse
response h(τ ;t) can be obtained as follows
X
h(τ ; t) =
αi (t)δ(t − τi (t))
(2.5)
i
Where αi (t) is the complex attenuation factor of path i. In most urban scenarios the scattering environment is rich and there are many scattering paths and
the case in which none is more dominant than the other i.e Non line-of-sight (NLOS)
scenario, then according to central limit theorem h(τ ; t) can be modelled as zero mean
complex valued Gaussian random process and the received envelope |h(τ ; t)| at any
5
instant t has Rayleigh probability density function (pdf). Therefore the channel is
modelled as Rayleigh fading channel in this case. In the presence of fixed scatterers
or one dominant path i.e line-of-sight(LOS) scenario, in addition to moving scatterers the h(τ ; t) cannot be modelled as having zero mean and hence envelop envelope
|h(τ ; t)| can be modelled as random process with Ricean distributed pdf, hence the
fading caused in LOS scenario will be Ricean fading [5].
2.1.2
Time variance of the channel
Multi-path channel becomes time variant due to the relative motion between the
transmitter and the receiver. This relative motion causes the frequency of the received
signal to be shifted relative to that of the transmitted signal. This Doppler shift f d ,
is proportional to the velocity of the receiver and the frequency of the transmitted
signal, and the maximum doppler shift is formulated as follows [5].
Vmax fc
(2.6)
c
Where Vmax is the maximum velocity of the mobile and c is the speed of light. Since
the received signal travels over several reflected paths and each path have different
doppler shift hence multi-path propagation creates spectral or Doppler spreading. A
characterization of time variant channel in time domain is coherence time Tc . It is
the measure of the expected time duration over which the channel response remains
essentially invariant [3]. The coherence time could also be defined in terms of 90
percent or 50 percent correlation time. Time variation of the channel results into
slow or fast fading on the channel. For instance if the coherence time of the channel
is less than the symbol transmission time Ts , the channel is known as fast fading
channel. However, if coherence time is larger than Ts , the channel is known to be
slowly fading channel. A study of coherence time estimation done in [17] for the fixed
wireless channel with parameters NLOS, LOS, Street view (SV) and no street view
(NSV) reveals the time stability of fixed wireless channel. Following are the results
fd =
Table 2.1: Coherence time Tc [sec]
LOS/NSV
3.9
LOS/SV
1.2
NLOS/NSV
3.8
NLOS/SV
1.1
Power spectral density (PSD) is an important parameter which shows the
spread of signal power in frequency domain. It is obtained according to definition
by taking the Fourier transform of the autocorrelation function of the received signal
relative to the delay parameter τ . The autocorrelation function (ACF) is defined as
follows
Ar (τ1 , τ2 ; t, ∆t) = E[r∗ (τ1 ; t)r(τ2 ; t + ∆t)]
(2.7)
6
Where r(τ1 , τ2 , t; t + ∆t) are the observations of the signal r (t) for durations
τ1 and τ2 at time instants t and t + ∆t. An important assumption here is that the
received signal r (t) passes through a channel which is wide sense stationary (WSS).
Most channels in practice are WSS, such that the joint statistics of a channel measured
at two different times depends only on the time difference [15]. The PSD is obtained
by taking Fourier transform of ACF.
Sr (f ) = F {Ar (τ )}
(2.8)
Where F{.} denotes Fourier transform. The power spectrum according to Jakes [15]
is the usual spectrum that is observed for a mobile, with a plausible assumption used
by Jakes that the angle of arrival of multi-paths are uniformly distributed. Jakes
spectrum with a maximum doppler spread fd is shown in Figure 2.2.
(a)
(b)
Figure 2.2: Jakes Spectrum and its assumption.
7
2.2
State of the art
The complex path gain of any radio channel can quite generally be represented as
having a fixed component plus a fluctuating (or scatter) component. The former might
be due to a line-of-sight path between the transmitter and the receiver; the latter is
usually due to echoes from multiple local scatterers, which causes variations in space
and frequency of the summed multipath rays. The spatial variation is translated into
a time variation when either end of the link is in motion. In the case of fixed wireless
channels, time variation is a result of scatterers in motion.
2.2.1
Ricean K-Factor
If the scatter component has a complex Gaussian distribution, as it does in the
central limit (many echoes of comparable strength), the time-varying magnitude of the
complex gain will have a Ricean distribution. The key parameter of this distribution
is the Ricean K-Factor (or just K ), which is the power ratio of the fixed and scatter
components [6]→[8]. It is a measure of the severity of fading. The case K =0 (no fixed
component) corresponds to the most severe fading, and in this limiting case, the gain
magnitude is said to be Rayleigh distributed. From the earliest days, most analyses
of mobile cellular systems, e.g. [9], have assumed Rayleigh fading. What alters
complex path gain is the slow motion of scatterers along the path, e.g., pedestrians,
vehicles, and wind-blown leaves and foliage. As a result, the path gain at any given
frequency will exhibit slow temporal variations as the relative phases of arriving echoes
change. Because these variations are often slight, the condition of a dominant fixed
component plus a smaller fluctuating component takes on a higher probability than
that for mobile links. Furthermore, since temporal variations can occur on many
scatter paths, the convergence of their sum to a complex Gaussian process is plausible.
Therefore, we can expect a Ricean distribution for the gain magnitude, with a higher
K-factor, in general, than that for mobile cellular channels. The complex path gain
g(t) and K-factor model formulated according to [10] and [11] is as follows
g(t) = V + v(t)
(2.9)
Where V is the fixed complex value and v (t) is the random time varying part
because of the scattering environment effects with variance σ 2 . The K factor for
Ricean fading is defined as the ratio of static power to the time varying power
|V |2
(2.10)
σ2
A comprehensive measurement campaign to study of K-factor dependence on
height, wind, beam-width and seasons is done in [10] and [11]. K-factor is modelled
as a random variable with log-normal distribution and a zero mean and variance
depending upon the above mentioned factors.
K=
8
Figure 2.3: K-factor measurement results.
2.2.2
K-Factor Estimation
Various methods have been reported to estimate K using moments calculated from
time series, e.g. [12]→[14]. The simple and widely used method of moments from
[13] is as follows.
Suppose R(t) be the envelop of the received signal, and that R(t) follows Ricean
fading distribution i.e, the case in which we have a strong LOS component. According
to [13] the factor γ is defined as follows
γ=
V [R2 ]
(E[R2 ])2
(2.11)
Where V{.} denotes the variance and E{.} denotes the mean of the time series.
The K factor is derived as
√
1−γ
√
K=
(2.12)
1− 1−γ
2.2.3
Doppler Spectrum model for FWA channel
In the classical Jakes Doppler spectrum, the receiver (or the transmitter) is assumed
to move at a certain speed. However, in fixed wireless channel, both the transmitter
and the receiver are static and time-variations are actually due to moving scatterers.
A stochastic model for this sort of time-varying channels is introduced, which can be
employed to more accurately simulate the performance of fixed wireless communications [16]. Interestingly the Doppler spectrum is different for the fixed channel case.
It is peaky at centre and its shape depends on the maximum velocity of the scatterers
and number of multi-path components which are non-stationary. Another interesting
observation is that the bandwidth of Doppler spectrum is twice than that of Jakes
9
spectrum i.e it is 4fd . Figure 2.4 shows the Doppler spectrum and the parameter a is
a measure of static multi-path scatterer components.
Figure 2.4: Doppler spectrum for FWA channel.
10
Chapter 3
Methodology
In order to investigate the proposed questions, two simultaneous approaches should
be taken into account. On one hand we need to simulate the channel model and on
the other hand we need to evaluate the measured data. In this chapter we describe
the channel model used in the downlink measurements.
3.1
Channel model
Orthogonal frequency division multiplexing (OFDM) channel model is used in the
downlink measurements, as it is the multiple access scheme that is used in LTE
downlink. The figure 3.1 shows the transmitter part of OFDM [15].
Figure 3.1: OFDM transmitter
The input data stream is modulated by a QAM modulator, resulting in a
complex symbol stream X[0],X[1],...,X[N -1]. This symbol stream is passed through
a serial-to-parallel converter, whose output is a set of N parallel QAM symbols
X[0],...,X[N -1] corresponding to the symbols transmitted over each of the subcarriers.
Thus, the N symbols output from the serial-to-parallel converter are the discrete frequency components of the OFDM modulator output s(t) In order to generate s(t) ,
11
these frequency components are converted into time samples by performing an inverse
DFT on these N symbols, which is efficiently implemented using the IFFT algorithm.
The IFFT yields the OFDM symbol consisting of the sequence x [n]= x [0],...,x [N -1]
of length N , where
N −1
1 X
√
X[i] expj2πni/N ,
x[n] =
N i=0
0≤n≤N −1
(3.1)
This sequence corresponds to samples of the multicarrier signal i.e. the multicarrier signal consists of linearly-modulated subchannels, and the right hand side of
Equation 3.1 corresponds to samples of a sum of QAM symbols X [i ] each modulated
by an orthogonal carrier. The cyclic prefix is then added to the OFDM symbol, and
the resulting time samples are ordered by the parallel-to-serial converter and passed
through a D/A converter, resulting in the baseband OFDM signal x̃ (t).
The transmitted signal is filtered by the channel impulse response h(t) and
corrupted by additive noise, so that the received signal is y(t) = x̃(t) ∗ h(t) + n(t).
This signal is down converted to baseband and filtered to remove the high frequency
components.
Figure 3.2: OFDM receiver
The A/D converter samples the resulting signal to obtain y[n] = x̃[n] ∗ h[n]
+ v [n]. The cyclic prefix from y[n] is removed as the first µ samples. The result is a
sequence y[n] of length N samples whose DFT without noise would be Y [i] = H[i]X[i]
. These time samples are serial-to-parallel converted and FFT is performed. This
12
results in scaled versions of the original symbols H[i]X[i] , where H[i] = H(fi ) is
the flat-fading channel gain associated with the ith sub-channel. The FFT output is
parallel-to-serial converted and passed through a QAM demodulator to recover the
original data.
The beauty of OFDM is that the wideband channel is decomposed into a set
of narrowband orthogonal sub-channels each having a different QAM symbol. The
equalization can be done in frequency domain with knowledge of each subchannel
gain X[i] = Y [i]/H[i]. A draw back for frequency domain equalization is the noise
enhancement as noise in each subchannel is enhanced by 1/H[i] factor.
3.1.1
OFDM channel estimation
Channel estimation is done with the help of pilot or reference symbols which are
placed according to LTE standard in the time-frequency grid. Pilots are positioned
according to the used number of MIMO antennas. The equation for independent
Figure 3.3: Reference symbols grid for 1 antenna
narrowband subchannels can be written as [17].
yk = hk xk + nk ,
k = 0.....N − 1
(3.2)
where hk is the complex channel attenuation given by h = [h0 h1 ...hN −1 ]T =
DFT N (g) and n = [n0 n1 ...nN −1 ]T . We can write in matrix notation as
y = XFg + n
(3.3)
13
Figure 3.4: Parallel narrowband subchannels
Figure 3.5: OFDM channel estimation structure
14
where X is the matrix with elements of x on its diagonals and (the DFT matrix
comes after that which is giving some error yet)
F=
is the DFT-matrix with
1
(3.4)
WNnk = √ e−j2πnk/N
N
The lease square estimate [17] for the impulse response g minimizes (y H
XFg) (y - XFg) and generates
ĥLS = FQLS FH XH y
(3.5)
QLS = (FH XH XF)−1
(3.6)
where
the equation 3.5 is reduced to
ĥLS = X−1 y
(3.7)
The least square estimator is also referred to as zero forcing estimator and has
high mean square error. In case of a fixed wireless channel scenario the least squares
estimate should be sufficient as there is not much variation in the channel state. The
vector x in simple terms consists of pilot sub-carriers spread over the band, and y
are the same pilot sub-carriers after passing through the channel. ĥLS would then
represent the channel transfer function (CTF) in frequency domain. In order to find
the channel impulse response (CIR) we need to take IFFT of the least squares channel
estimate [19] that is ĥLS .
CIR = IFFT(ĥLS )
(3.8)
For instance figure 3.6 shows the CTF and CIR from a measurement.
15
Channel transfer function
60
55
50
45
0
20
40
60
80
100
120
Pilot Subcarriers over 20MHz band 1:200
140
160
180
200
Channel impulse response
Absolute value CIR in dB
60
50
40
30
20
10
0
0
0.2
0.4
0.6
0.8
1
Channel response times [uSec]
1.2
1.4
1.6
1.8
Figure 3.6: CTF and CIR
3.2
Performance
Performance is always the key to qualify a wireless channel and its characterization.
How well wireless system is performing depends on some KPI (key parameter indications). We can judge the performance of a wireless system with many KPIs. Some of
most commonly used KPIs to characterize wireless system performance are capacity,
RSRP(Reference signal received power), RSRQ(Reference signal received quality),
SNR (Signal to noise ratio), doppler spread, error probability, throughput, coverage,
maximum number of users, cell edge rates etc. The important KPI for us in this
study will be SNR, capacity and doppler spread. We will also look at the variance of
individual subcarriers over the band which is normally not a well known KPI but in
our case of channel characterization based on the scatters and small scale movements
in channel with different scatter size serves our purpose and reveals useful information
about the channel.
16
3.2.1
Signal and noise power estimation
The calculation of Noise and Signal power is necessary to estimate the SNR at the
receiver, which is important metric to analyse the performance. It is used in the
calculation of capacity, also it is a measure of how good is the channel condition. We
know from our signal processing basics the definition of power
PX = E[kXk2 ]
(3.9)
In our case for the OFDM system the Noise power is calculated by measuring
the power on unallocated subcarrier. By an unallocated subcarrier I mean it does not
contain a pilot symbol neither it is allocated for any user data. We know from our
allocation scheme the indices which have no allocation. Let k be a subcarrier which is
used as a pilot and n be a subcarrier which has no allocation. Then The noise power
would simply be power received on subcarrier n as there is no contribution from any
other source on this subcarrier than noise. Let Pn denote the noise power and Pk
denote the received power on pilot subcarrier, then we can calculate the signal power
Ps easily using
Ps = Pk − P n
(3.10)
And the SNR ρ in dB could be calculated using
ρ = 10log(
3.2.2
Ps
)
Pn
(3.11)
Subcarrier power variance
An interesting parameter to study small scale movements in environments is subcarrier power variance. For 20MHz bandwidth there are 200 pilot subcarriers, so we
will measure the variance of the power on each subcarrier over measurement time
to see which carriers are affected more than others. If we can specify a bandwidth
most affected by small scale movements in the environment. For s = 1,2,...200 Ps is
the received signal power per subcarrier from which we have already subtracted the
estimated noise power, refer to 3.10, let us denote T as the measurement duration in
TTI (ms). The Variance power per pilot subcarrier in dB over time will be
var[Ps ] = 10log[
T
X
(Ps − mean(Ps )2 )]
(3.12)
We will use 3.12 in our channel evaluation for the indoor and outdoor measurements.
17
3.2.3
Capacity
By definition of information theory capacity of wireless channel is the upper bound
on rate at which information can be reliably transmitted over the wireless channel.
Similarly we interpret capacity provides the limiting rate of the amount of information
that can be transferred though the channel with a negligible amount of error probability. Shannon the father of information theory had provided a mathematical model
of the capacity which could be used to calculate the capacity of a given channel. It
is common to represent the channel capacity within a unit band-with of the channel.
The channel capacity is then measured in bits/s/Hz. Different kinds of transmission
schemes could be possible when we want to transmit information, and it is desirable
to use the one which maximizes the capacity. If we nominate input of and output of
a memoryless channel respectively X and Y random variables.
Figure 3.7: Channel Capacity
The channel capacity is mathematically defined as using definition from [23]
C = maxp(x) I (X;Y)
(3.13)
where I (X;Y) represents the mutual information between X and Y. The equation 3.13 means that mutual information I has to be maximum over all the possible
statistical distributions of X and Y. We are aware about the definitions of the mutual
information and entropy so they need not be explained here. Here is the general and
mathematical concept about channel capacity.
A useful deduction of the generalized capacity model could be derived for additive white Gaussian noise. in our performance calculation we will use the deduction
of MIMO channel capacity which is given by [24]
ρ
HH∗ ]
(3.14)
N
where M are the number of RX antennas and N are number of TX antennas,
H is the complex MIMO channel matrix of size M x N, IM is the identity matrix of
size M and ρ is the average SNR on each RX antenna.
From this generalized MIMO capacity equation 3.14 we can find out the SISO
capacity by setting M = N = 1,
C = log2 det[IM +
C = log2 [1 + ρ||h||2 ]
(3.15)
where h is the complex channel gain between the single existing TX-RX path.
18
3.3
Effect of Imperfect CSI on channel performance
Let us assume a valid scenario when the FWA channel CSI which is assumed to be
known at the transmitter end has changed and now we model the effect of this delta
in the channel as . The least square equalizer which is a zero forcing equalizer will
force the known channel h so a residue h will remain with the equalized signal.
y=
h−
x+n
h
(3.16)
y = [1 − ]x + n
(3.17)
h
To keep it simple and study the effect of residue h , let us assume that the
modulation scheme used is antipodal signaling. The antipodal signal constellation is
given in fig.3.8 The ML error probability is found out by recalling derivation from
Madhow text book [25]
r
2Eb
Pe,M L = Q(
)
(3.18)
No
Where Eb is the energy per bit and No is the Gaussian Noise power. Now this
is implied assuming that channel ’h’ did not include any unknown imperfect CSI,
from the equation 3.17 it is clear that along with input signal the energy of factor
needs to be included. The equation 3.18 will
which is along with input signal x h−
h
take the form in this case
s
||[1 − h ]||2 2Eb
Pe,M L = Q(
)
(3.19)
No
r
2Eb
)
(3.20)
Pe,M L = Q([1 − ]
h
No
Now we study the effect of the this important ratio h by simulation in matlab.
Eb
With different
SNR levels and sweep the ratio h from 0 to 3 and keeping a fixed
No
SNR at a time. It shows that error probability saturates to maximum soon when the
ratio is more than 1 meaning the channel is completely unknown at this time when
grows bigger than channel state h. Also we observe that with high SNR values the
curve becomes more steep.
19
Figure 3.8: Antipodal signaling constellation
Pe with different SNR
1
0.9
0.8
0.7
−10 dB
− 5 dB
0 dB
5 dB
10 dB
15 dB
20 dB
Pe,ML
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.5
1
1.5
Epsi / h
2
Figure 3.9: Performance with Imperfect CSI
2.5
3
20
Chapter 4
Simulation
In this chapter we start by simulating an OFDM transceiver, to gain an in depth
working idea of all the modules involved. The transceiver is then tested for performance by calculating symbol error rate(SER) curves for Gaussian, Rayleigh and
Rician fading distributions. Next we simulate the fixed wireless channel model and
produce Doppler spectrum plots for different cases with varying velocity of scatterers.
4.1
OFDM transceiver chain
In this simulation we only include baseband part rather than in actual scenario according to Figure 3.1 passband is included to modulate the baseband signal to a
certain carrier frequency.
Table 4.1: OFDM Simulation parameters
Parameter
Sampling Frequency
FFT size
Cyclic prefix
No. of symbols for each SNR
Channel type
Value
30.72 MHz
256
4 samples
1024
AWGN, Rayleigh, Rician
Figure 4.1: OFDM baseband transmitter
21
The Figure 4.1 shows the transmitter modules and xb is the baseband signal
produced at the end of transmitter chain. Data in form of bits R is available at the
start which needs to be transmitted at transmitter and recovered at the receiver. This
data is converted to QAM complex symbols and these symbols are then converted
from serial to parallel. N number of QAM symbols are fed at the S/P block where N is
the size of the FFT. After conversion of complex symbols to parallel IFFT operation
is performed and time domain signal x is obtained. Next we add a cyclic prefix which
is just the repetition of a number of starting samples extended at the end. The length
of cyclic prefix could be variable but importantly should be greater than maximum
delay spread of the channel to avoid inter symbol interference. Cyclic prefix length
has been standardized for LTE.
The signal xb is then passed through fading channel and AWGN noise is added.
Three fading channels are simulated i.e Gaussian, Rayleigh and Rician fading channel.
This noisy and faded baseband signal is then fed to the receiver.
Figure 4.2: Baseband signal through channel
y=x∗h+n
(4.1)
The noisy faded baseband signal is then fed to the receiver. According to Figure
4.3, First we remove the cyclic prefix according to the number of samples set at the
transmitter. Then after serial to parallel conversion FFT operation is performed to
get the signal in frequency domain. The frequency domain signal Y is fed to the
equalizer which performs frequency domain zero forcing equalization i.e dividing the
faded noisy signal with the recovered channel Y/H with important assumption of
perfect channel knowledge at the receiver. The zero forcing equalizer is simple but
it has a draw back of noise enhancement. Next the equalized signal is fed to the
detector which performs maximum likelihood detection. With assumption of equal
symbol probability for each symbol of the constellation ML detector works on the
principle of defining decision boundaries.
In Figure 4.4 The green lines represent the decision boundaries for QAM modulated symbols. Any received symbols that falls in their respective areas will be
detected accordingly. The ML detector is also extendible to higher order of QAM or
for other types of symbol modulations. After parallel to serial conversion X̂ is the
22
Figure 4.3: OFDM baseband receiver
Figure 4.4: ML detector
23
detected QAM symbols sequence which is sent to QAM demodulator to recover the
original bit stream which was transmitted.
Symbol error probability curves for QPSK(4−QAM)
0
10
theory−QPSK
simulation−AWGN
simulation Rayleigh
simulation Rician
−1
Symbol Error Rate
10
−2
10
−3
10
−4
10
−5
10
0
5
10
15
20
Es/No, dB
25
30
35
40
45
Figure 4.5: Symbol error rate curves
The symbol error rates are calculated for the three types of channels with
varying SNR in Figure 4.5. The theoretic and simulated AWGN produced follow
each other showing the method is concise. The Rayleigh and Rician symbol error
rates show a similar behaviour but Rician fading has lower errors because of a LOS
component.
24
4.2
Doppler spectrum fixed wireless channel
In this section we simulate behaviour of multipath fading channel through the generation of time series, and their statistical analysis by producing Doppler spectrum
curves to gain more insight into this phenomenon. To achieve this goal, we introduce
a multiple point-scatterer model based on a Cartesian coordinate system. We are able
to simulate Rayleigh and Rician fading with a sum-ray model in which there is a fixed
base station and a fixed mobile station. By a ray we mean a complex exponential
with a certain wavelength depending upon carrier frequency. As the scatter moves it
introduces a change in frequency which depends upon the distance the wave travels
from transmitter to receiver. The multi point scatterers are able to move because of
which there is a change in path which each ray takes after reflecting from a certain
scatterer.
rk = e−j2πfc tk
= e−j2πfc
−j2π
=e
Dk
c
Dk
λc
(4.2)
= e−jkc Dk
where vector rk represents a ray which travels from transmitter to k th scatterer
to the receiver, fc is the carrier frequency, kc = 2π
is the constant for convenience
λc
th
and Dk is the distance vector for k scatterer.
For a Rayleigh fading channel without any LOS component, R is the envelop
signal at receiver.
R=
k
X
rk
(4.3)
However for a Rician fading channel, where Dd represents a direct ray from
transmitter to receiver and a0 is the magnitude of direct signal
R=
k
X
rk + a0 e−jkc Dd
(4.4)
The power spectrum of the received envelop signal R represents the doppler
spectrum, which can be mathematically calculated by taking the absolute squared of
the fourier transformed autocorrelation function of R
Pd = |FFT(ACFR )|2
(4.5)
The Figure 4.6 shows a realization of the model with scatterers in a circle
to form uniform arrival angle distribution, an important condition for the Doppler
spectrum according to Clarke [20]. The triangle represents a base station and mobile
25
Table 4.2: Scatter simulation parameters
Parameter
Value
Sampling Frequency
1 KHz
Carrier Frequency
2GHz
Number of time samples
10000
Number of scatterers
100
Radius of fixed scatterers
200m
Distance between BS and MS
1000m
FFT size
1024
Channel type
Rayleigh, Rician
station is at the centre of circle whose position remains fixed. Now there are two
types of scatterers, one which are moving and other one which remain still. We select
a given number of moving scatterers and place them initially at random positions
within the circle, which move with time at random velocities with a certain maximum
velocity. We can set the number of moving scatterers as a factor of the total number
of scatterers and the maximum velocity of movement is adjustable.
We do an analysis based on varying maximum velocity of scatterer and the
number of moving scatterers and observe interesting results that Doppler spectrum
is widened by increasing the maximum velocity and also by increasing the number of
scatterers which are moving.
26
500
400
Propagation scenario. Distance (m)
300
200
100
0
−100
−200
−300
−400
−500
−200
0
200
400
600
Propagation scenario. Distance (m)
800
1000
Figure 4.6: Scatter simulation model
27
Figure 4.7: Doppler spectrum analysis
28
Chapter 5
Measurements and Field trials
In this chapter we describe the field trials for downlink measurements done with
LTE advanced test-bed at TU-Dresden. We further describe the measurement setup
for indoor and outdoor measurements. The motivation was to find out a measure
of stability of wireless channel in a scattering environment. Measurement campaign
included initial indoor measurements and a downlink outdoor and uplink outdoor
measurement. From the relay deployment point of view an optimum antenna height
and position for best performance and an efficient channel state feedback scheme.
The LTE standard channel state feedback scheme provides an overhead considering
that for a fixed wireless channel it is deduced from the measurements evaluation that
channel state does not vary as much over time. So there is an opportunity to gain
advantage of the fixed relay scenario and utilize some reference OFDM symbols for
data which are used for channel feedback in standard.
5.1
Test-bed equipment
The measurement equipment for field trials was SORBAS LTE advanced test equipment by Signalion for research purpose [21]. Detail on SORBAS test setting and
possibilities is available in appendix. The test equipment can be used as a UE as well
as an enB with a change in firmware and layer 1/layer 2 level signal analysis could
be performed. It can be used for uplink and downlink measurement purposes and
generates a dump file for the measurement duration already set. The dump file is
stored on an attached computer for evaluation. Figure 5.1 shows the SORBAS test
equipment. Two antenna elements per UE and per enB can be attached via cable
depending on the used MIMO configuration.
5.2
Indoor measurements
Initial measurements were done indoor to verify the stability of test-bed and initial
evaluation to check stability of channel under static condition. Further measurements
were done with one person moving, two persons moving and three persons moving in
the lab. Initial motivation was to identify if possible the taps in the channel impulse
response to certain scatterers with observation of over time. The Figure 5.2 shows the
indoor measurement setup and Figure 5.3 shows dimensions of indoor environment.
The Table 5.1 shows the important measurement parameters.
29
Figure 5.1: SORBAS test equipment
30
Figure 5.2: Indoor measurement environment
31
Figure 5.3: Lab dimensions and movement description
32
Table 5.1: Indoor Measurement parameters
Parameter
Value
Sampling Frequency
30.72 MHz
Measurement type
Downlink
Antenna elements
2 per enB and 2 per UE
Antenna polarization
Cross polarized
MIMO setting
2x1 (Dump taken on 1 UE antenna)
Measurement duration
2100 TTI (2.1 sec)
Measurement Scenarios Static, 1 person moving, 2 persons moving, 3 persons moving
Antenna transmit power
10.4 dBm per antenna measured with spectrum analyser
During indoor measurements campaign there were several different scenarios
tested.
• Static environment
• 1 person moving in defined direction as in figure 5.3
• 2 persons moving in defined direction as figure 5.3
• 4 persons randomly
• 1 person moving along with the board
We have observed that the channel becomes more and more chaotic as we add
more scatters in the environment. We look at the CTF through the measurement
duration to see the channel behaviour while moving scatters. We further look at the
variance mean variance over the measurement duration of subcarriers to understand
which are the subcarriers most affected by the small scale movements. This will
give us an insight about the frequencies affected by small scale movements. For sure
more and more statistics will be required to strengthen the claims done during the
measurement evaluation but the current observation are based on the few iterations
that are listed above.
The CTF of all the iterations are given from figure 5.4 to figure 5.7
33
Figure 5.4: Indoor Static environment
Figure 5.5: Indoor 1 person moving environment
34
Figure 5.6: Indoor 2 persons moving environment
Figure 5.7: Indoor 4 persons moving environment
35
5.3
Outdoor measurements
After successful indoor measurement campaign, the integrity of test equipment was
tested to be working fine. A downlink Outdoor measurement campaign was carried
out in Dresden area. The motivation was to find out ideal height and location of a
relay considering deployment scenario based on performance and channel correlation
characteristics. The scatterers were basically cars on the road whose impact was
studied on the channel with increasing height. Figure 5.8 shows the map of the testbed in Dresden. The 3 enB sectors cooperating during the measurement are Hbf Sud
60o (8), Hbf 240o (3) and Lenneplatz 180o (6). Two configurations were measured for
each antenna height
• enB antenna downtilt 7o
• enB antenna downtilt 15o
Figure 5.8: Test-bed map
Each measurement dumps taken for a duration of about 1 sec and evaluation
is done with a post processing dump analysis tool where we estimate the channel and
36
calculate the important metrics. The configuration for the outdoor measurements are
listed in the table 5.2.
Table 5.2: Outdoor Measurement parameters
Parameter
Sampling frequency
Measurement type
Antenna elements
Antenna polarization
MIMO setting
Measurement duration
Rx Antenna height
Tx Antenna downtilt
Antenna transmit power
Value
30.72 MHz
Downlink
2 per enB and 2 per UE
Cross polarized
2x6
1 sec
2.5m, 4m, 5.5m, 7.5m
7o and 15o
10.4 dBm per antenna measured with spectrum analyser
Some captures from the outdoor measurement campaign are given in figure 5.9
and 5.10
5.3.1
Evaluation chain
The measurement dumps taken during the downlink campaign are evaluated in order
to estimate/equalize the channel and calculate important metrics. The dump made
from SORBAS is of the baseband signal which already includes the frequency error
corrections done by the frontend. So what we receive is a baseband signal ready to
be processed. The whole processing and evaluation MATLAB chain is summarized
in the figure 5.11
37
Figure 5.9: Measurement location and Van
38
Figure 5.10: Measurement location and Van
39
Figure 5.11: Post dump analysis chain
40
Chapter 6
Results
In this chapter we will discuss the important results based on the downlink indoor and
outdoor measurement campaigns. We see some interesting relation and observation
in wireless channel based on the small scale movements in environment as well as
with the antenna height.
6.1
Indoor measurements
The different iterations as explained in the Measurements chapter are done for indoor
trials. By Inspection of the CTF provided for the measurements we can deduce
important observations.
• Not all the frequencies are affected by the moving persons
• Only a certain range of subcarriers are affected around DC subcarrier (100),
by the inspection in plots figure 5.4 to figure 5.7, We see that approx 30 pilot
subcarriers are mostly affected around DC subcarrier.
It is an interesting result as it enables us to distinguish the band out of total
bandwidth which will be affected the most for small scale movements. Let’s say W
be the total bandwidth and γ be the factor of the bandwidth which is affected by
small scale movements, then
Ws = γW
(6.1)
where Ws is the part of total bandwidth W which is affected by scatter movements. By our OFDM knowledge we know that spacing between pilot subcarriers is
6*15KHz, so by inspection i.e 30th SC on both sides around DC subcarrier would
mean a bandwidth of 2*6*30*15KHz = 5.4MHz (Ws ) bandwidth is affected in this
s
=
indoor measurements scenario out of 20MHz(W), so the bandwidth factor γ = W
W
0.27.
It is desirable to have γ as small as possible because in that case we have a
fairly static channel and then CSI will then be reliable for longer time. The limitation
of this result is the statistics, we only had enough much time to do these 5-6 iterations
base on this we present these measurement results, it is desirable to carry out more
of similar experiments to have statistically significant data.
41
6.1.1
Sub-carrier power variance
The power variance of subcarriers as described in 3.12 is calculated from the evaluation
chain and the figure 6.1 shows the results for indoor iterations.
We can see that variance of the subcarriers around DC are actually more than
ones on the mid to end range of the bandwidth. We find out a mean curve for all the
variances combined and there is a peak around DC subcarrier with a width of 20-30
subcarriers. This also matches to the result we observed above in equation 6.1.
6.2
Outdoor measurements
[h] For the outdoor measurement we will also look at the power variance of subcarriers
also we will measure the performance with MIMO capacity .
6.2.1
Sub-carrier power variance
The power variance of subcarriers in outdoor environment shows that in an uncontrolled environment attenuation can not be limited to a specific bandwidth. The
variation is distributed over the band and the movements can not be limited to small
scale because environment can have many factors which cause the attenuation. There
are different size and shapes of scatters also could be many multipath sources. The
results shown in figure 6.2 shows the variance of each MIMO TxRx path. The solid
lines denote the RxAnt1 and the dotted lines denote the RxAnt2 for the same Tx. We
can see that mean variance is existing over the complete bandwidth and not over a
subset. It is observed that even in the case of no moving cars seen, still the subcarrier
power variance is distributed over the bandwidth, which is because of characteristic
of multipath and other scatters than the prime ones i.e road traffic. The possible
scatters causing this kind of distribution in attenuation could be e.g moving trees
because of wind. It shows us that outdoor environment can’t be completely static.
6.3
Capacity
Capacity is most important metric in performance qualification of a channel. The
theoretical background about capacity was discussed in section 3.2.3, here we present
the results of the capacity based on 5.2. We see a difference in capacity with a
downtilt angle changed from 7o to 15o mostly because the LOS component is reduced
and Fresnel zone is lost in this case, it impacts capacity as it directly impacts the
received SNR at the receiver antenna which is a function of capacity. We deduce
several interesting observation from figure 6.3.
• Generalized result show that capacity is higher for LOS MIMO wireless channel
than NLOS
42
• No cars study, shows relation between antenna height and capacity is such that
antenna height doesn’t affect capacity which is more or less consistent for the
downtilt 7o , however for downtilt 15o we see a trend of increasing capacity with
height. We can deduce that with strong LOS component capacity is independent
of Rx Antenna height, however with weak LOS component increasing height
improves the capacity.
• The moving cars study, shows that moving scatters introduce the diversity effect for LOS MIMO wireless channel and capacity numbers are unexpectedly
higher in the presence of scattering environment, however for weak LOS channel (downtilt 15o ), Capacity remains consistent with increasing height. Notable
point for a peak in downtilt 15o with cars measurement at 2.5m height as it is
a near LOS channel condition.
43
60
NoMove
Move1
Move2
MoveBoard
Move3dir
Move4
AvgVar
50
Variance dB
40
30
20
10
0
0
20
40
60
80
100
Pilot Subcarriers
120
140
160
180
200
Figure 6.1: Indoor environment SC power variance
44
12
10
Variance dB
8
6
4
2
0
0
20
40
60
80
100
Pilot Subcarriers
120
140
160
180
Figure 6.2: Outdoor environment SC power variance
200
45
MIMO Capacity calculation using eq.3.14
33
MIMO 2x6 Capacity (bps/Hz)
32
LOS, no moving cars
LOS, with moving cars
NLOS, no moving cars
LOS, with moving cars
31
30
29
28
27
2.5
3
3.5
4
4.5
5
5.5
increasing antenna height (m)
6
6.5
Figure 6.3: Measured MIMO capacity
7
7.5
46
Chapter 7
Conclusions
In this chapter we will summarize important conclusions which are drawn from the
scatter simulation, indoor and outdoor measurements.
7.1
Concluding remarks
• Doppler spectrum analysis fig. 4.7 shows that doppler spectrum bandwidth
increases with increasing number of moving scatters, as well as increasing the
speed of moving scatters. This means that when there are more scatters around
e.g moving people, moving cars, wind blowing trees etc. the channel will become
more varying in time as coherence time is inversely related to doppler spread
and the duration for which channel state remains consistent will be lesser. The
same relation holds with increasing speed of scatters.
• Realtime measurement shows that channel becomes increasingly chaotic by increasing moving scatters this confirms the simulation results which are presented
in the first conclusion above. Also another observation shows small scale movements contribute to the channel attenuation to a maximum factor of γ = 0.27
of the total bandwidth. The derivation is well explained in results section 6.1.
• MIMO channel capacity increase in NLOS condition in relation to antenna
height whereas in LOS condition it is mostly independent of the height. This
conclusion is applicable on the height of the receiver antenna/relay keeping the
same condition of scatters. This conclusion has been deduced as a result from
capacity plot in fig. 3.14.
• Scatters improve the channel capacity in LOS channel condition, whereas in
NLOS condition the presence of scatters show no significant effect on the capacity of channel. This conclusion has been deduced as a result from capacity
plot in fig. 3.14.
7.2
Future directions
We have studied the affects of moving scatters on the FWA channel, several interesting
facts are concluded. The studies could be further improved by improving statistics
of the measurements, as the results are based on limited number of iterations. The
aim to possibly identify a specific channel tap in CIR for a moving scatter so it could
help in efficient cancellation of scattering attenuation couldn’t be achieved, because
maybe the goal was too optimistic and channel doesn’t behave like that. This could
47
be further studied in future to stress on the time domain effects of scatters. The
control of the scatters could be improved and even more defined movements could
be tested and studied. Measurement of imperfect channel state should be done in
realtime so that we can tell at what point in time the state becomes bad enough so
that no more decoding is possible.
48
Bibliography
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[2] Cisco visual networking index (VNI), Global mobile data traffic forecast(20112016)
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PTR Second Edition, 2002
[4] Tafzeel UR Rehman.A, Link Reliability in Cooperative Relaying Using Network
Coding PhD Dissertation
[5] J. G. Proakis,Digital Communications. McGraw-Hill International Edition,
2001
[6] D. Greenwood and L. Hanzo, Characterization of mobile radio channels, in
Mobile Radio Communications, R. Steele, Ed. London, U.K.:Pentech, 1992,
pp. 163185.
[7] M. Schwartz, W. R. Bennett, and S. Stein, Communication Systems and Techniques. New York: McGraw-Hill, 1966, sec. 9.2.
[8] J. D. Parsons, The Mobile Radio Propagation Channel. Hoboken, NJ: Wiley,
1992, pp. 134136.
[9] W.C.Jakes,Jr.,Ed., Microwave Mobile Communications.NewYork: Wiley, 1974
[10] Ricean K-Factors in Narrow-Band Fixed Wireless Channels: Theory, Experiments, and Statistical Models Larry J. Greenstein, Life Fellow, IEEE, Saeed S.
Ghassemzadeh, Senior Member, IEEE, Vinko Erceg, Fellow, IEEE, and David
G. Michelson, Senior Member, IEEE
[11] Time Variability of the Foliated Fixed Wireless Access Channel at 3.5 GHz
D. Crosby, V.S. Abhayawardhana , I.J. Wassell , M. G. Brown , M.P. Sellars
Cambridge Broadband Ltd., Selwyn House, Cowley Rd., Cambridge CB4 OWZ,
UK. dcrosby,[email protected]]
[12] L. J. Greenstein, D. G. Michelson, and V. Erceg, Moment-method estimation
of the RiceanK -factor, IEEE Commun. Lett. , vol. 3, no. 6, pp. 175176, Jun.
1999.
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[13] C. Tepedelenlioglu, A. Abdi, and G. B. Giannakis, The Ricean K factor: Estimation and performance analysis, IEEE Trans. Wireless Commun. , vol. 2, no.
4, pp. 799810, Jul. 2003.
[14] Y. Chen and N. C. Beaulieu, Maximum likelihood estimation of the K factor
in Ricean fading channels, IEEE Commun. Lett. , vol. 9, no. 12, pp. 10401042,
Dec. 2005.
[15] Goldsmith, Andrea, Wireless Communication
[16] Steven Thoen, Liesbet Van der Perre, and Marc Engels , Member, IEEE Modeling the Channel Time-Variance for Fixed Wireless Communications
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channels at 2.5 GHz, in Proc. of IEEE ICPWC00, December 2000
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9571000
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1
Appendix A
SORBAS test equipment
A.1
AVAILABLE SORBAS TEST UE OPTIONS
Basis Option FDD (UE Category 1,2,3)
High Throughput Option FDD (UE Category 4)
Basis Option TDD (UE Category 1,2,3)
High throughput option FTD (UE Category 4)
Multi UE (simulation of up to 64 UEs with one Sorbas Test UE)
A.2
DUPLEX MODES
FDD and TDD is supported (Test UE options)
A.3
RADIO FREQUENCY
FDD: Band 1-14, 17-19
UHF 790-862 MHz(Digital dividend)
TDD: Band 33-40
UHF 790-862 MHz(Digital Dividend)
A.4
PHYSICAL LAYER FEATURES
Standard Specification: Signalion will provide ongoing standard adaptation.
The UEPHY implementation is according to 3GPP Release 8:
TS 36.201 ,TS 36.211 ,TS 36.212 ,TS 36.213 ,
A.4.1
Downlink Characteristics
OFDM including HARQ;
Modulation QPSK, 16 QAM, 64 QAM;
SISO, SFBC , RX diversity,2x2 MIMO
A.4.2
Uplink Characteristics
SC-FDMA UL
Modulation QPSK, 16 QAM;
2
A.4.3
PhysicalSignals and Channels
P-SCH/ S-SCH / RS;
PBCH;
PRACH;
PUSCH; PUCCH,
PDSCH; PDCCH;
PCFICH;
PHICH
SRS;
A.5
LAYER 2 FEATURES
Standard Specification: Signalion will provide ongoing standard adaptation.
A.5.1
The UE MAC and RLC implementation according to 3GPP Release 8
TS 36.321, TS 36.322
MAC: Channel multiplexing,
Random Access,
HARQ support,
Priority handling,
Buffer status reporting
RLC: Transparent Mode (TM)
Unacknowledged Mode (UM)
Acknowledged Mode (AM);
PDCP: Part of the protocol test Option
Transport Channels: RACH;
UL-SCH;
DL-SCH;
PCH;
BCH/D-BCH
TRITA -ICT-EX-2015:135
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