# Iterative Channel-Tracking Techniques for 5.9 GHz  DSRC Applications

Authors: Harb Abdulhamid; Kemal E. Tepe; Esam Abdel-Raheem
Journal: Research Letters in Communications
 (2008)

---

## Abstract

This letter proposes a novel channel-tracking scheme to improve the performance of the dedicated short-range communication (DSRC) systems affected by rapid fluctuations in channel envelopes. The proposed technique is called “iterative (turbo) compensation.” It utilizes additional information extracted from the receivers output to further improve the accuracy of the channel estimation. Simulation results show that the iterative scheme performs better than noniterative techniques in higher constellation modulations at high vehicle speeds.


---

## Body

## 1. Introduction

Dedicated short-range communication (DSRC) was established
at a 5.9 GHz band  for services involving
vehicle-to-vehicle and
vehicle-to-roadside  communications [1–3].
The physical layer design of DSRC was adopted from the popular wireless local
area network (WLAN) standard IEEE 802.11a [4] with high delay spread due to mobility in particularly
urban canyons [5]. It
was shown in [6] that
conventional channel tracking, which is based solely on the known preamble of
each packet, is highly sensitive to velocity, data rate, and the packet length,
and that conventional channel estimation is not feasible for DSRC applications.
A new channel-tracking technique, called the first-order channel tracking, was
investigated in [7, 8]
for highly mobile orthogonal frequency division multiplexing (OFDM) systems
based on different reference signals. The first-order channel-tracking
technique was more effective in combating with channel impurities than
conventional techniques where only preamble was used in channel estimation.
Similar studies in [9, 10] had also
showed improvement in mobile environments by utilizing first-order
decision-aided schemes along with numerous other studies outlined in [7]. The benefits of these
schemes can be applied to other OFDM applications that involve mobility. The
ultimate goal is to maximize the data throughput by minimizing packet
retransmission via reduction of the packet error rate (PER).In this paper, a novel iterative channel-tracking
technique is proposed to further enhance the packet error performance at high
velocities. The performance of the iterative technique is compared to
conventional channel estimation and the first-order scheme in a DSRC system
under varying signal-to-noise ratio (SNR), velocity, and modulation schemes.


## 2. Background on DSRC

The DSRC physical layer utilizes OFDM [11]. OFDM can be realized in
baseband with inverse discrete Fourier transforms (IDFTs). Note that a
transmitted OFDM symbol, Xn=[Xn,0,Xn,1,…,Xn,N−1], is a vector consisting of N parallel data symbols in frequency domain,
where n is the symbol index. DSRC uses 64 frequency
subcarriers with only 52 subcarriers actually used for signal transmission. Of
the 52 subcarriers, 4 are pilot channels and they are used for phase tracking,
while the remaining 48 subcarriers are used for data carriers. Each DSRC packet
consists of two preambles. The first preamble consists of ten short training
symbols for packet detection, frequency offset estimation, and symbol timing.
The second preamble, consists of two identical training symbols, (xtrain), used for channel estimation subsequent to a
long guard interval of length GCE=3.2 μs. The data rate is then determined only by
selecting a modulation scheme and coding rate. The transmission mode and packet
length is determined adaptively based on SNR, but the preamble is always
modulated with binary phase-shift keying (BPSK).The channel is modeled using statistical models
presented in [12]. Rayleigh
fading channels are used to represent 2D isotropic scattering environments to
consider the worst-case performance of the receiver.


### 2.1. Conventional Receiver and Channel Estimation

At the conventional receiver, the guard interval is
removed from the received signal, then the received data is converted to
parallel form, which is denoted asyn,k. At this point, yn,k is demultiplexed into the fast Fourier
transform (FFT), yielding the following output in the frequency
domain:(1)FFT[yn,k]=Yn,k=Hn,k⋅Xn,k+Wn,k,where Hn,k denotes the channel frequency response at the nth symbol index of the kth subcarrier, Wn,k represents the additive white Gaussian noise
(AWGN), and Xn,k is related to data that used to be the input
to the inverse FFT (IFFT) of the transmitter. If it is assumed that Xn,k is known, then the channel response can be
solved from (1) as follows:(2)Hn,k=Yn,k−Wn,kXn,k=Yn,kXn,k−Wn,kXn,k=H^n,k+ψn,k,where H^n,k is the estimated channel response based on the
least-square (LS) method with an error component of ψn,k due to the AWGN. Therefore, the accuracy of
the channel estimation decreases with noise power. This effect is known as noise
enhancement. In order to reduce the effect of noise enhancement, the
channel estimator employs the second preamble, which consists of two identical
training symbols. The estimated channel response is computed as the average
channel response over the first and second received OFDM symbols Y−1,k and Y−2,k:
(3)H^0,k=Y−2,k+Y−1,k2⋅Xtrain,k.In conventional channel estimation, it is assumed that
the channelH^n,k exhibits time-invariant fading, and can be
approximated as H^0,k for the duration of the packet. Then the
received data is compensated as follows:
(4)Y^n,k=Yn,kH^0,k.That has shown to be not
effective [6] and the
following sections will investigate alternative approaches that are necessary
to improve receiver performance in DSRC.


### 2.2. First-Order Channel Tracking

Adaptive signal processing concepts can be considered
for tracking the channel variation by updating the estimated channel responseH^n,k for n=1,2,…,N−1 [13]. The receiver block diagram that enables the channel
tracking is shown in Figure 1. The initial channel estimate, H^0,k,
is obtained based on the training data. Assume that the first received symbol Y0,k is compensated by the initial channel
estimate, that is,(5)Y^0,k=Y0,kH^0,k.After the received symbol is
compensated, the desired signal, X^1,k, is produced from the feedback circuit and the rest of the packet duration. The
desired data, X^n,k, is used to update the channel estimate symbol-by-symbol. Preliminary channel
estimate obtained by feedback loop in the receiver, denoted as H˜n,k, is obtained by the LS method:(6)H˜n,k=Yn,kX^n,k.
In this process, noise enhancement caused by the error components can be reduced by introducing a
“forgetting factor,” (γ), which is a constant to balance between H˜n,k and H^n,k. The latter estimate is obtained recursively from H^0,k by introducing the channel estimate error ΔHn,k defined as(7)ΔHn,k=H˜n,k−H^n,k.
Hence, the recursive equation can be put in the form
(8)H^n+1,k=H^n,k+γ⋅ΔHn,k=(1−γ)⋅H^n,k+γ⋅H˜n,k.
Forgetting factor γ gets its values between 0 and 1 with an
optimal value depending on the time invariant properties of the channel such as
velocity, SNR, and the modulation scheme.Figure 1
Receiver block diagram.


## 2.1. Conventional Receiver and Channel Estimation

At the conventional receiver, the guard interval is
removed from the received signal, then the received data is converted to
parallel form, which is denoted asyn,k. At this point, yn,k is demultiplexed into the fast Fourier
transform (FFT), yielding the following output in the frequency
domain:(1)FFT[yn,k]=Yn,k=Hn,k⋅Xn,k+Wn,k,where Hn,k denotes the channel frequency response at the nth symbol index of the kth subcarrier, Wn,k represents the additive white Gaussian noise
(AWGN), and Xn,k is related to data that used to be the input
to the inverse FFT (IFFT) of the transmitter. If it is assumed that Xn,k is known, then the channel response can be
solved from (1) as follows:(2)Hn,k=Yn,k−Wn,kXn,k=Yn,kXn,k−Wn,kXn,k=H^n,k+ψn,k,where H^n,k is the estimated channel response based on the
least-square (LS) method with an error component of ψn,k due to the AWGN. Therefore, the accuracy of
the channel estimation decreases with noise power. This effect is known as noise
enhancement. In order to reduce the effect of noise enhancement, the
channel estimator employs the second preamble, which consists of two identical
training symbols. The estimated channel response is computed as the average
channel response over the first and second received OFDM symbols Y−1,k and Y−2,k:
(3)H^0,k=Y−2,k+Y−1,k2⋅Xtrain,k.In conventional channel estimation, it is assumed that
the channelH^n,k exhibits time-invariant fading, and can be
approximated as H^0,k for the duration of the packet. Then the
received data is compensated as follows:
(4)Y^n,k=Yn,kH^0,k.That has shown to be not
effective [6] and the
following sections will investigate alternative approaches that are necessary
to improve receiver performance in DSRC.


## 2.2. First-Order Channel Tracking

Adaptive signal processing concepts can be considered
for tracking the channel variation by updating the estimated channel responseH^n,k for n=1,2,…,N−1 [13]. The receiver block diagram that enables the channel
tracking is shown in Figure 1. The initial channel estimate, H^0,k,
is obtained based on the training data. Assume that the first received symbol Y0,k is compensated by the initial channel
estimate, that is,(5)Y^0,k=Y0,kH^0,k.After the received symbol is
compensated, the desired signal, X^1,k, is produced from the feedback circuit and the rest of the packet duration. The
desired data, X^n,k, is used to update the channel estimate symbol-by-symbol. Preliminary channel
estimate obtained by feedback loop in the receiver, denoted as H˜n,k, is obtained by the LS method:(6)H˜n,k=Yn,kX^n,k.
In this process, noise enhancement caused by the error components can be reduced by introducing a
“forgetting factor,” (γ), which is a constant to balance between H˜n,k and H^n,k. The latter estimate is obtained recursively from H^0,k by introducing the channel estimate error ΔHn,k defined as(7)ΔHn,k=H˜n,k−H^n,k.
Hence, the recursive equation can be put in the form
(8)H^n+1,k=H^n,k+γ⋅ΔHn,k=(1−γ)⋅H^n,k+γ⋅H˜n,k.
Forgetting factor γ gets its values between 0 and 1 with an
optimal value depending on the time invariant properties of the channel such as
velocity, SNR, and the modulation scheme.Figure 1
Receiver block diagram.


## 3. Iterative Channel Tracking

Although first-order scheme performed better than conventional approach [7, 8], in this section further improvement, called iterative
channel tracking, will be investigated. This iterative technique utilizes
additional information to further improve the accuracy of the tap weight
estimates with additional complexity. The iterative technique is derived based
on (8). Recall that H˜n,k is the channel response based on the LS
estimate in (6). In (5), the received data, Yn,k,
is compensated  by the channel estimate, H^n,k. The current desired signal is used to estimate the current channel response H˜n,k. The updated estimated channel response, H^n+1,k, is used to compensate the next received data, Yn+1,k. Recall that at high velocities, the channel may exhibit
fast-fading characteristics, which means that there
may be a substantial change in the channel response during the symbol duration.
Therefore, by the time the next symbol is received, the estimated channel
response may be slightly outdated.The proposed solution to this problem is to iterate
the compensation of the received symbol with the next channel estimate. This
technique involvesiterative tracking, where the knowledge of an
additional feedback is used to improve the current channel estimate, and hence
improve the overall system performance to better track the rapid channel
variations. Such iterative schemes like Turbo Decoding are known to be
very effective in improving performance of communication systems [14].The technique is derived as follows. First,Yn,k is received and compensated by H^n,k to be demapped, deinterleaved, and decoded.
The Viterbi decoder in the receiver circuit reduces errors that are due to
outdated channel estimates and produces the desired signal, X^n,k(0). This desired data is used to estimate the preliminary channel estimate H˜n,k(0), using the least-square method in the following relation:(9)H˜n,k(0)=Yn,kX^n,k(0).
The next step is to obtain a first channel estimate using the following relation:
(10)H^n,k(0)=(1−γ)⋅H^n,k+γ⋅H˜n,k(0),
where 0<γ<1 is the forgetting factor which is selected
based on simulation practice. At this point, the received symbol Yn,k is compensated a second time with the current
channel estimate, H^n,k(0).
After decoding the compensated data, a new desired signal is produced, X^n,k(1). Again, the new desired data is used to estimate the next preliminary channel
estimate:(11)H˜n,k(1)=Yn,kX^n,k(1).
The channel response is finally
estimated based on the recursion in (10) using the new
estimate:(12)H^n,k(1)=(1−γ)⋅H^n,k(0)+γ⋅H˜n,k(1),where H^n,k(0) is obtained from (10). The recursive channel
update is then simplified as follows: (13)H^n,k(1)=(1−γ)⋅[(1−γ)⋅H^n,k+γ⋅H˜n,k(0)]+γ⋅H˜n,k(1)=(1−2⋅γ+γ2)⋅H^n,k+(γ−γ2)⋅H˜n,k(0)+γ⋅H˜n,k(1)=γ2⋅(H^n,k−H˜n,k(0))+γ⋅(H˜n,k(1)+H˜n,k(0)−2⋅H^n,k)+H^n,k.
Finally, the second channel estimate is used at the next symbol index:
(14)H^n+1,k=H^n,k(1),where H^n+1,k is used to compensate the next symbol Yn+1,k, which then will produce a new desired signal X^n+1,k(0) and so on. This continues until the last
symbol of the packet. The pseudocode for the technique is given in
Figure 2. In the case of fast-fading
channels, the second compensation is crucial since there may be a substantial
change in the channel response during a symbol interval. As a result, the
iterative tracking will reduce the packet error rate at high velocities.Figure 2
Iterative channel-tracking technique's pseudocode.
(1)procedure ITV. CH. TRAKING(Xtrain,k,Yn,k,γ)(2)   ComputeH^0,k     ▹ estimate based on preamble(3)   ComputeY^0,k  ▹ compensate first received symbol(4)   GenerateX^1,k(5)   Selectγ(6)H^1,k=H^0,k(7)forn=1:(N−1)do(8)fori=0:1do(9)     ComputeH˜n,k(i)           ▹ Estimate(10)    ComputeH^n,k(i)            ▹ Update(11)    ComputeY^n,k(i)          ▹ Compensate(12)    GenerateX^n,k(i)(13)end for(14)Y^n,k=Y^n,k(1)(15)H^n+1,k=H^n,k(1)(16)end for(17)end procedure


## 4. Simulation Results

The DSRC physical layer was simulated in Matlab. The performance is measured in terms of
packet error rate (PER). Conventional, first-order, and iterative channel estimation
techniques are compared in a 5.9 GHz DSRC system.
Transmissions of 10,000 packets were sent per
simulation under varying SNR (0–30 dB) and velocities (0–240 km/h).
A velocity of 240 km/h would be the legal maximum
of relative vehicular velocity. All simulations were conducted under 1-path
Rayleigh fading and had a fixed packet length ofN=64 OFDM symbols per packet. The forgetting
factors are selected for each modulation scheme based on the simulation
practice. Extensive trials were carried out to select a forgetting factor that
yields the best overall PER performance. The forgetting factor, γ, for QPSK, 16- and 64-quadrature amplitude modulation (QAM) selected to be 0.1,
0.4, and 0.5, respectively. These forgetting factors do not necessarily yield
the best BER, but they would achieve the best overall PER over the range of
velocity and SNR discussed.Figure3 presents results for 16-QAM.
More results are reported in [8]. The iterative algorithm outperforms first-order
channel estimation method at SNR greater than 20 dB. Figure 3(b) plots the PER
versus velocity at a fixed SNR of 30 dB. At relative velocities greater than 75 km/h,
iterative channel estimation begins to outperform the first-order method.
Notice that the added performance enhancement increases with the increase of
velocity. Similar results were obtained for 64-QAM modulations. Iterative
technique does not improve QPSK modulation results substantially, but it always
performs comparable to the first-order scheme.
Therefore, iterative  technique is recommended for higher-order modulation
schemes at high vehicle velocity operations.QAM16: first-order versus iterative under rayleigh fading.
(a)
PER versus SNR at 238 km/h(b)
PER versus velocity at 30 dB


## 5. Conclusion

A novel iterative channel estimation scheme that
involves iterative compensation has been proposed and has shown
added enhancements to the system through simulation studies that it
provided. Substantial  improvements
are obtained in 16-QAM and 64-QAM transmissions,
when the relative velocities are greater than 75 km/h. It is
important to note that the results of this are not limited to 5.9 GHz DSRC. There
are many different types of OFDM systems that may employ such channel estimation techniques.



---

*Source: 485013-2008-04-07.xml*