Chapter 1 Introduction
1.3 Motivation
As wireless communications continues to enjoy phenomenal growth, the ever rising demand for higher data-speeds coupled with the advent of popular bandwidth-hungry applications such as High-Definition (HD) video are putting pressure on wireless communication systems to offer higher data rates.
However, data rates of current wireless systems are still limited to several tens of Mbps-hampered by congestion and limited frequency spectrum in their current frequency bands of operation. Since the key to higher data rates is bandwidth, the most promising path to multi-Gbps wireless communication is the use of mm-wave frequencies where very large bands of frequency spectrum are available [11]. For instance, the FCC’s 60-GHz band offers 7 GHz of unlicensed spectrum (57-64 GHz). However, mm-wave wireless networking presents many technical challenges owing to the high carrier frequencies and the wide channel bandwidths used [12]–[16]. The challenges include the significantly higher air-link loss (about 30 dB higher at 60 GHz than at 2.4 GHz), and reduced device performance and lower power efficiency. In addition, the wide channel bandwidth means higher noise power and reduced signal-to-noise ratio (SNR). All these factors make wireless networking at 60-GHz “pico-cellular” in nature with the radio cells typically smaller than 10 m. Consequently, multi-gigabit-per-second wireless networking at 60 GHz requires an extensive high-capacity feeder network to interconnect the large number of radio access points.
Figure 1-3 Concept of future wireless home network system based on RoF techniques.
Radio-over-Fiber (RoF) technology can provide the required feeder network as it is best suited to deal with the demands of small-cell networks [17]-[19]. A fiber-based Distributed Antenna System (DAS) has the special advantage that it can support the transparent distribution of multiple wireless standards or applications. Figure 1-3 illustrates a cartoon for future 60-GHz wireless home network based on RoF technology. Because of the high path loss and high attenuation through building walls, in-building radio cells at 60 GHz are confined to a single room. This reduces user interference resulting in very high wireless data capacity per user [20].
In order to achieve multi-standard operation, 60-GHz RoF systems must be able to handle wireless signals with different requirements. For instance, for 60-GHz systems, both single-carrier (quadrature amplitude modulation (xQAM)) and multi-carrier (orthogonal frequency-division multiplexing (OFDM)) modulation formats are important. The two formats may impose different system performance requirements on the 60-GHz RoF systems. For instance channel uniformity is very critical for single-carrier systems [21]-[22].
Here we try to use Digital Signal Processing (DSP) to compensate the un-even frequency response and therefore increase signal performance.
Figure 1-4 Opportunity of 60-GHz RoF system
GHz
Chapter 2
Single Sideband Signal by Hilbert Transform
2.1 Introduce of Hilbert Transform
The Hilbert transform H[g(t)] of a signal g(t) is defined as
1 1 g( ) 1 g(t- )
The Hilbert transform of g(t) is the convolution of g(t) with the signal 1/πt. It is the response to g(t) of a linear time-invariant filter (called a Hilbert transformer) having impulse response 1/πt. The Hilbert transform H[g(t)] is often denoted as g^(t) or as [g(t)]ˆ
A technicality arises immediately. The alert reader will already be concerned with the definition (1) as the integral is improper: the integrand has a singularity and the limits of integration are infinite. In fact, the Hilbert transform is properly defined as the Cauchy principal value of the integral in (1), whenever this value exists. The Cauchy principal value is defined—for the first integral in (1)—as
We see that the Cauchy principal value is obtained by considering a finite range of integration that is symmetric about the point of singularity, but which excludes a symmetric subinterval, taking the limit of the integral as the length of the interval approaches ∞ while, simultaneously, the length of the excluded interval approaches zero. Henceforth, whenever we write an integral as in (1), we will mean the Cauchy principal value of that integral (when it exists).
Fourier
The signal 1/(πt) has Fourier transform
j, if f 0
If g(t) has Fourier transform G(f ), then, from the convolution property of the Fourier transform, it follows that g (t) has Fourier transform
ˆG(f)=-j sgn(f)G(f)
Thus, the Hilbert transform is easier to understand in the frequency domain than in the time domain: the Hilbert transform does not change the magnitude of G(f ), it changes only the phase. Fourier transform values at positive frequencies are multiplied by − j (corresponding to a phase change of −π/2) while Fourier transform values at negative frequencies are multiplied by j (corresponding to a phase change of π/2). Stated yet another way, suppose that G(f ) = a + b j for some f . Then G(f ) = b − a j if f > 0 and G(f ) = −b + a j if f <
0. Thus the Hilbert transform essentially acts to exchange the real and imaginary parts of G(f ) (while changing the sign of one of them).
Single-sideband Modulation
be two complex-valued signals associated with g(t). The significance of these two signals can be seen from their Fourier transforms. We have
1 ˆ 1 2 1
G (f) = [G(f) + j G(f)] = [G(f)-j sgn(f)G(f)] = G(f) [1 + sgn(f)]= G(f) u (f)
2 2 2
+
where u(f) is the unit step function, and, similarly,
G (f) = G(f) u (-f)
−Thus g+(t) has spectral components (equal to those of g(t)) at positive frequencies only, i.e., g+(t) has a right-sided spectrum. Similarly, g−(t) has spectral components (equal to those of g(t)) at negative frequencies only and hence has a left-sided spectrum. These spectra are illustrated in Fig. 2-1.
Figure. 2-1 Signal spectra: (a) G(f) (b) the right-sided spectrum G+(f) (c) the left-sided spectrum G−(f).
It is now straightforward to express upper- and lower-sideband signals in terms of g+(t) and g−(t). Let g(t) be the modulating signal, assumed bandlimited to W Hz, and let fc > W be the carrier frequency. In the frequency domain, the upper sideband signal is given by
USB + c c
S (f) = G (f - f ) + G (f + f ),
c − cand the lower sideband signal is given by
LSB c + c
S (f) = G (f - f )+ G (f + f ),
− c cas sketched in Fig. 2-2 below.
Figure. 2-2 Single-sideband spectra: (a) upper-sideband, (b) lower-sideband
It follows from the frequency-shifting property of the Fourier transform that
USB + c - c
c c
c c
S (f) = g (t) exp( j 2f t) + g (t) exp(-j 2f t)
1 1
ˆ ˆ
= (g(t) + j g(t)) exp( j 2f t) + (g(t) - j g(t)) exp(- j 2f t)
2 2
1 ˆ 1
= g(t) [exp( j 2f t) + exp(-j 2f t)] + g(t) [j exp( j 2f
2 2 c c
c c
t) - j exp(-j 2f t)]
= g(t) cos(2f t) - g(t) sin(2f t)ˆ A similar derivation shows that
LSB c ˆ c
S (f) = g(t) cos(2f t) + g(t) sin(2f t)
Thus we see that single-sideband modulation can be regarded and implemented as a form of quadrature amplitude modulation (QAM), with the modulating signal g(t) placed in the in-phase channel and the Hilbert transform of g(t) (or its negative) placed in the quadrature channel. A block diagram illustrating this approach is given in Fig. 2-3.
Figure. 2-3 Generation of an SSB-modulated signal
Reference：
F. R. Kschischang, The Hilbert Transform,
Department of Electrical and Computer Engineering, University of Toronto, http://www.comm.toronto.edu/frank/papers/hilbert.pdf
2.2 The Structure of Single Sideband Signal
Figure 2-4 shows the concept of spectra processing of the proposed SSB-QPSK. This modulator consists of two SSB modulators. The configuration of the SSB modulator is shown in Fig. 2-5. Figure 2-4(a) shows the spectra of the I-axis and the Q-axis in conventional QPSK modulation. To realize two times higher throughput, QPSK needs a two times wider bandwidth.
(Fig. 2-4(b))
A single side band of the two times wider QPSK is same as the full bandwidth of QPSK. (Fig. 2-4(c)) One of the two SSB signals is turned over on the frequency axis and shifted by a half bandwidth for superimposition. (Fig.
2-4(d))
Figure. 2-4 Multiplication of LSB and USB
0 IF ω2 t
⎛ω ⎞
⎜ ⎟
⎝ + ⎠
ωIF 0
IF ω2 t
⎛ω − ⎞
⎜ ⎟
⎝ ⎠
Figure. 2-5 A block diagram of the proposed orthogonal SSB-QPSK
The operation principle of the proposed orthogonal SSB-QPSK method is described using Fig. 2-5. The transmitting signal ν(t) passes through a serial to parallel converter and produces two output signals. The upper branch is a multiplier of the input signal and the carrier cosω1t. The lower branch is a multiplier of Hilbert transformed input signal and the π/2 shifted carrier sinω1t.
The throughput can be twice the conventional QPSK, and the paralleled two signal sequences will be referred to as Ik and Qk, respectively. These two signals pass through Nyquist filters and produce Nyquist wave formed signals.
The signal Ik is modulated as the LSB output and is multiplied with ω1 − ω0/2, which is a subtraction of the radio carrier frequency ω1 and a half of the baseband symbol frequency ω0. At the same time, the signal Qk is modulated as the USB output and is multiplied with ω1 + ω0/2, which is an addition of ω1 and a half of the baseband symbol frequency ω0, where ω0 = πT is the angular
frequency, and T is the symbol period.
2.3 Matlab Simulation of Single Sideband Signal
0 1 2 3 4 5
Figure 2-6 Electrical spectrum of (a) Traditional double sideband signal (b) Single sideband signal of I and Q (c) combination of I and Q single sideband
signals
According to the block diagrams in Fig. 2-5 a simulation was done. The conditions of simulation are as follows. Figures 2-6 (a), (b) and (c) show the simulation results of the spectrum characteristics orthogonal SSB-QPSK. This simulation results approve that the modulation is an SSB type and has
orthogonal spectrum allocation.
Fig. 2-6 (a) shows the traditional Double Sideband QPSK signal spectrum.
After Hilbert transform process, we can produce a single sideband signal for I and Q signals (Fig. 2-6 (b)). Fig. 2-6 (c) is the combination of single sideband I, Q signals.
Chapter 3
Introduction of Equalizer
3.1 What Is Inter-symbol Interference
One of the practical problems in digital communications is inter-symbol interference (ISI), which causes a given transmitted symbol to be distorted by other transmitted symbols. ISI occurs because of the channel which has an amplitude and phase dispersion. This dispersion causes the signal to interfere with another parts of the signal. This effect causes to ISI. The ISI is imposed on the transmitted signal due to the band limiting effect of the practical channel, un-even channel frequency response and also due to the multi-path effects of the channel.
Figure 3-1 Concept of ISI
3.2 Introduction of Equalizer
One of the most commonly used techniques to counter the channel distortion (ISI) is channel equalization. The equalizer is a filter that provides an approximate inverse of the channel response. Since it is common for the channel characteristics to be unknown or to change over time, the preferred embodiment of the equalizer is a structure that is adaptive in nature.
Conventional equalization techniques employ a pre-assigned time slot (periodic for the time-varying situation) during which a training signal, known in advance by the receiver, is transmitted. In the receiver the equalizer coefficients are then changed or adapted by using some adaptive algorithm (e.g.
LMS, RLS, etc.) so that the output of the equalizer closely matches the training sequence. However, inclusion of this training sequence with the transmitted information adds an overhead and thus reduces the throughput of the system.
Therefore, to reduce the system overhead, adaptation schemes are preferred that do not require training, i.e., blind adaptation schemes. In blind equalization, instead of using the training sequence, one or more properties of the transmitted signal are used to estimate the inverse of the channel. Figure 3-2 depicts the response to a single transmit pulse at various points in the system.
Figure 3-2 Transmission process with example pulse responses
3.3 Concept of Feed-forward Equalizer and Decision Feedback Equalizer Symbol-Spaced Equalizers (Feed-forward Equalizer)
A symbol-spaced linear equalizer consists of a tapped delay line that stores samples from the input signal. Once per symbol period, the equalizer outputs a weighted sum of the values in the delay line and updates the weights to prepare for the next symbol period. This class of equalizer is called symbol-spaced because the sample rates of the input and output are equal. Below is a schematic of a symbol-spaced linear equalizer
Figure 3-3 Schematic of a symbol-spaced linear equalizer
Decision-Feedback Equalizers
A decision-feedback equalizer is a nonlinear equalizer that contains a forward filter and a feedback filter. The forward filter is similar to the linear equalizer described in Symbol-Spaced Equalizers, while the feedback filter
equalized signal. The purpose of a DFE is to cancel inter-symbol interference while minimizing noise enhancement. By contrast, noise enhancement is a typical problem with the linear equalizers described earlier.
Below is a schematic of a fractionally spaced DFE with L forward weights and N-L feedback weights. The forward filter is at the top and the feedback filter is at the bottom. If K is 1, the result is a symbol-spaced DFE instead of a fractionally spaced DFE.
Figure 3-4 Schematic of a decision feedback equalizer
In each symbol period, the equalizer receives K input samples at the forward filter, as well as one decision or training sample at the feedback filter.
The equalizer then outputs a weighted sum of the values in the forward and feedback delay lines, and updates the weights to prepare for the next symbol period.
3.4 Algorithm
Figure 3-5 Sorts of equalizers
In the design of equalizers there exist different types of design criteria [25].
The most frequently encountered two criteria with their efficiency are told in the sequel. Some equalizers are designed to minimize mean square error (MSE) at the slicer input with the constraint of zero ISI. These are called Zero-Forcing (ZF) equalizers. Some equalizers are designed to minimize the MSE at the input of the slicer by reducing the signal slightly at the slicer input. This reduction of signal results in reduction in MSE, so overall MSE is smaller than that of the ZF equalizer. These equalizers are called MSE equalizers. The MSE
enhancement.
The linear equalizer is cheap in implementation but its noise performance is not very good. So, in the literatures, some equalizer types which introduce nonlinearity are searched. The most popular of these nonlinear equalizers is the decision feedback equalizer (DFE). The DFE is first proposed by Austin in [27].
This equalizer results in less MSE against linear equalizer, but it has the disadvantage of error propagation in its feedback loop.
As it is told before, most of the time, the channel’s and, consequently, the transmission system’s transfer functions are not known. Also, the channel’s impulse response may vary with time and fade. The result of this is that the equalizer can't be designed a priori, frequently. So, mostly preferred scheme is to exploit adaptive equalizers. Adaptive equalizers use adaptive algorithms to converge to the true coefficients and have the benefit of tracking the changes in the channel impulse response. But, to achieve this, it adds additional complexity to the receiver structure.
Also, the adaptation algorithm plays a significant role for the performance of the equalizer. The most popular algorithm from the aspect of performance and complexity is the Least Mean Squares (LMS) algorithm. It has a good performance and low complexity. It is globally convergent if the desired values are given correctly. The handicap of LMS algorithm for equalizer if the desired symbols are not correct, it does not converge. So, the equalizer using LMS algorithm requires a priori known symbols in case the decisions of the equalizer are wrong.
The LMS algorithm is a linear adaptive filtering algorithm that belongs to the family of the stochastic gradient algorithms [26]. The stochastic gradient
algorithms differ from the steepest descent algorithms in that the gradient is not calculated deterministically. The LMS algorithm has two parts. In the first part, the output of a transversal filter is computed according to the tap inputs and the error term is generated according to the difference between the filter output and the desired response. In the second part, the adjustment of the tap weights is done according to the error term.
The algorithm forms a feedback loop by the error term fed back. The filter produces an output and the difference between the output and the desired term is obtained. This difference is the estimation error term. The estimation error is given to the Adaptation Control Block. Adaptation Control Block multiplies the estimation error with the input taps’ complex conjugate and a step size α.
The results of the corresponding taps are added to the corresponding filter taps.
So, the new filter is obtained [24]:
( ) ( ) ( ) ( ) ( ) ( )
T ˆ where f(k) is the filter vector at time k, and rˆ* (k) is the complex conjugate of the input vector at time k, α is the step size parameter, e(k) is the estimation error, I(k) is the desired response at time k. In equation (2.3), fn(k) is the nth tap of the filter at time k, and r*(k-n) is the complex conjugate of the input at time k-n, and other parameters are the same as first equation. Equations (2.2) and (2.3) are equivalent. The small step size will result in less excess error but in slow convergence rate. The large step size will result in high excess error but high convergence rate.Chapter 4
The Concept of New Optical Modulation System
4.1 Preface
There are three parts in optical communication systems : optical transmitter, communication channel and optical receiver. Optical transmitter converts an electrical input signal into the corresponding optical signal and then launches it into the optical fiber serving as a communication channel. The role of an optical receiver is to convert the optical signal back into electrical form and recover the data transmitted through the lightwave system. In this chapter, we will do an introduction about the external Mach-Zehnder Modulator (MZM), constructing a model of new ROF system.
4.2 Mach-Zehnder Modulator (MZM)
Direct modulation and external modulation are two modulations of generated optical signal. When the bit rate of direct modulation signal is above 10 Gb/s, the frequency chirp imposed on signal becomes large enough. Hence, it is difficult to apply direct modulation to generate microwave/mm-wave.
However, the bandwidth of signal generated by external modulator can exceed 10 Gb/s. Presently, most RoF systems are using external modulation with Mach-Zehnder modulator (MZM) or Electro-Absorption Modulator (EAM).
The most commonly used MZM are based on LiNbO3 (lithium niobate) technology. According to the applied electric field, there are two types of LiNbO3 device : x-cut and z-cut. According to number of electrode, there are two types of LiNbO3 device: dual-drive Mach-Zehnder modulator (DD-MZM)
and single-drive Mach-Zehnder modulator (SD-MZM) [6].
4.3 Single-drive Mach-Zehnder modulator
The SD-MZM has two arms and an electrode. The optical phase in each arm can be controlled by changing the voltage applied on the electrode. When the lightwaves are in phase, the modulator is in “on” state. On the other hand, when the lightwaves are in opposite phase, the modulator is in “off ” state, and the lightwave cannot propagate by waveguide for output.
4.4 The architecture of ROF system
4.4.1 Optical transmitter
Optical transmitter concludes optical source, optical modulator, RF signal, electrical mixer, electrical amplifier, etc.. Presently, most RoF systems are using laser as light source. The advantages of laser are compact size, high efficiency, good reliability small emissive area compatible with fiber core dimensions, and possibility of direct modulation at relatively high frequency.
The modulator is used for converting electrical signal into optical form.
Because the external integrated modulator was composed of MZMs, we select MZM as modulator to build the architecture of optical transmitter.
There are two schemes of optical transmitter generated optical signal. One scheme is used two MZM. First MZM generates optical carrier which carried the data. The output optical signal is BB signal. The other MZM generates optical subcarrier which carried the BB signal and then output the RF signal, as shown in Fig. 4-1 (a). The other scheme is used a mixer to get up-converted
shown in Fig. 4-1 (b). Fig. 4-1 (c) shows the duty cycle of subcarrier biased at different points in the transfer function.
Figure 4-1 (a) and (b) are two schemes of transmitter and (c) is duty cycle of subcarrier biased at different points in the transfer function. (LO: local oscillator)
4.4.2 Optical signal generations based on LiNbO3 MZM
Figure 4-2 Optical microwave/mm-wave modulation scheme by using MZM.
The microwave and mm-wave generations are key techniques in RoF systems. The optical mm-waves using external MZM based on double-sideband (DSB), single-sideband (SSB), and double-sideband with optical carrier suppression (DSBCS) modulation schemes have been demonstrated, as shown in Fig. 4-2. Generated optical signal by setting the bias voltage of MZM at quadrature point, the DSB modulation experiences performance fading problems due to fiber dispersion, resulting in degradation of the receiver sensitivity. When an optical signal is modulated by an electrical RF signal, fiber chromatic dispersion causes the detected RF signal power to
DSB
SSB
Dual-driveMZM
DSBCS
Single-drive MZM Single-drive
MZM
have a periodic fading characteristic. The DSB signals can be transmitted over several kilo-meters. Therefore, the SSB modulation scheme is proposed to
have a periodic fading characteristic. The DSB signals can be transmitted over several kilo-meters. Therefore, the SSB modulation scheme is proposed to