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Fast performance prediction of power controlled HSUPA with channel estimation
EURASIP Journal on Wireless Communications and Networking volume 2012, Article number: 148 (2012)
Abstract
Transmit power control (TPC) is used in high speed uplink packet access (HSUPA) to compensate for the near far effect which degrades system performance. However, its use in joint application with turbo coding, and hybrid automatic repeat request (HybridARQ) is very prohibitive and time consuming. In this article, we propose a simplified simulation methodology for power controlled HSUPA with HybridARQ Chase combining considering the effect of channel estimation on the system performance. The proposed method was tested on Rake and chiplevel linear minimum mean squared error (LMMSE) receivers. Simulation results show that the CPU time taken to reach the required performance is significantly reduced. Moreover, when the channel estimation is taken into account with an important number of pilot symbols, the system performance is close to that obtained with perfect channel acknowledgement (PCA).
1. Introduction
Transmit power control is necessary for high speed uplink packet access (HSUPA) system to reach the expected quality of service. It is jointly performed with HybridARQ in which ARQ technique is combined with turbo coding. Furthermore, adaptive modulation and coding, and the multicode transmission principle are used to provide high data rates. System performance, which is determined by the block error rate (BLER), depends on selected technology (channel coding, interleaving, modulation, and channel impulse response). The BLER is computed by simulating HSUPA technologies, according to 3GPP (3^{rd}Group Partnership Project), with MonteCarlo method [1]. However, iterative processes such as turbo decoding and transmitted power adjustment make this simulation very prohibitive and time consuming. As a solution, performance prediction has been proposed in many studies. This prediction aims to give an abstraction of the system performance over a multipath channel by summarizing the turbo code performance as lookup tables (reference curves). In [2], Kim et al. used a convex metric method that maps the received signal to noise ratio (SNR) to a Gaussian capacity. Its disadvantage is that it requires a significant memory storage since it uses more than one reference curve as shown in [3]. The method we have proposed in [4] considers one reference curve assuming Gaussian assumption (GA) at the detector output. Furthermore, it assumes perfect channel state knowledgement at the receiver side. The GA assumption is also used in [5] for extending the fast performance prediction (FPP) to iterative interference cancelation in multiuser MIMO CDMA. In [6], a prediction method has been proposed for coded MIMOOFDM systems.
This article deals with a FPP for HSUPA system, considering TPC in joint application with HybridARQ Chase combining. In our methodology, the focus is put on the channel estimation effect on the prediction process. In addition, we try to assess the TPC robustness against a noisy channel.
Under block fading frequency selective channel hypothesis, and GA assumption on the detector output, the analytical signal to interferenceplusnoise ratio (SINR) is equivalent to the SNR when the transmission is done through an additive white Gaussian noise (AWGN) channel. Hence, for HSUPA performance prediction over block fading frequency selective channel, FPP makes use of the analytical SINR with previously stored lookup tables (LUT). These tables are built with a turbo coded HSUPA simulator over AWGN channel. The rest of this article is organized as follows: HSUPA system model is described in Section 2. The FPP with TPC and HybridARQ Chase combining is introduced in Section 3. The impact of channel estimation on performance prediction is presented in Section 4. Simulation results and the conclusion are given in Sections 5 and 6, respectively.
2. HSUPA system model
2.1. HSUPA transmitter
As shown in Figure 1, the user data stream is transmitted with K parallel enhanced dedicated physical data channels (EDPDCHs). The control information need for data detection is carried by the newly introduced enhanced dedicated physical control channel (EDPCCH). The HSUPA control channel (HSDPCCH) is used for transmitting the channel state to the receiver (nodeB). The enhanced data channels are always timemultiplexed with the uplink control channel, DPCCH, which carries known pilot bits for channel estimation, and signal to interference ration (SIR) estimation purposes at the receiver side. A part of transmitted control information on the DPCCH is reserved for downlink power control as shown on the frame structure of DPCCH which is presented in Figure 2.
The spreading process is performed using orthogonal variable spreading factor (OVSF) codes. The code length is different from one physical channel to another. In order to compensate for this difference, the signal on each channel is weighted by a gain factor: β_{ed,k}for k th EDPDCH, β_{ ec }for EDPCCH, β_{ hs }for HSDPCCH, and β_{ c }for DPCCH. Then, the IQ mapping is done with iq coefficient which takes the value j for Q branch mapping, or 1 for I branch mapping. After IQ multiplexing, the complexvalued multicode chip sequence is scrambled with the long scrambling code S_{dpdch,n}. The scrambled multicode chip is expressed as
where E_{ c }is the chip energy, c_{ ed,k }is the spreading code, of length SF_{ ed,k }, used on the k th EDPDCH.
2.2. Sliding window model
Assume that the transmission is made through a multipath frequency selective channel, of length L, represented by a matrix H, and consider a sliding window of length 2 × E + 1. The received signal, associated to the m th transmitted multicode chip is given by
When assuming receive diversity with N_{ r }antennas, the received signal is expressed according to this sliding window model
where ${\mathbf{y}}_{m}^{i}={\left[{y}_{mE}^{i},\dots ,{y}_{m}^{i},\dots ,{y}_{m+E}^{i}\right]}^{T}$ is the received signal when the vector x_{ m }= [x_{mEL+1}, ..., x_{ m }, ..., x_{ m+E }]^{T}is transmitted through the multipath frequency selective channel, of length L, represented by (2E + 1) × (2E + L) Toeplitz matrix H^{i}(i = 1, ..., N_{ r }) given by
${h}_{l}^{i}\left(0\le l\le L1\right)$ are the complex coefficients of the channel connecting the transmitter antenna with the i th receiver antenna. ${\mathbf{n}}_{m}^{i}={\left[{n}_{mE}^{i},\dots ,{n}_{m}^{i},\dots ,{n}_{m+E}^{i}\right]}^{T}$ is a vector of complex AWGN samples each has zero mean and onesided spectral density N_{0}.
3. Fast performance prediction
3.1. Prediction principle
The prediction strategy principle is presented in Figure 3. The HybridARQ Chase combining approach is considered with N_{ t }transmissions. The TPC which is necessary for HSUPA system is also taken into account. It is done separately for each HybridARQ transmission. The main hypothesis of the prediction method is the Gaussian approximation at the receiver filter output. With this assumption, the SINR at the filter output is equivalent to that of an AWGN channel. Hence, the HSUPA performance in AWGN channel can be exploited as LUT for performance abstraction in a multipath channel [7]. The abstraction accuracy depends on the SINR estimation at the filter output. Hereafter, we derive the analytical SINR expressions for both Rake and LMMSE receivers. Furthermore, an analytical SINR after HybridARQ Chase combining is given.
3.2. SINR estimation
The receiver filter output corresponding to the m th chip is expressed as
where w is the vector, of length N_{ r }(2E + 1), containing the receiver filter coefficients, and (.)^{H}denotes the hermitian operator. Let us introduce a vector G such that $\mathbf{G}={\mathbf{w}}^{H}\mathbf{H}=\left[{g}_{0},\phantom{\rule{0.3em}{0ex}}\dots ,{g}_{{N}_{r}\left(2E\right)}\right]$. Thus, the SINR at the output of the receiver filter per HybridARQ transmission can be written as
The vector of the receiver filter coefficients, w depends on the selected detector. Hence, when the rake receiver is used, this vector is expressed as
where e is a vector of zero components except the (E + L)th element which is equal to 1. When the detection is done with an LMMSE detector, the vector containing its coefficients are given by
where H_{E+L}is the (E + L)th column of channel matrix H.
Once the SINR is computed according to (3.2), it is compared to a target SINR, SINR_{ T }. The TPC commands are then generated as follows:
where cmd refers to command.
After TPC commands generation, the transmit chip power is adjusted (see Figure 3) in response to these commands: if TPC_{ cmd }= "0", the transmit power is reduced by δ dB which is the power control step size. When TPC_{ cmd }= "1" the transmit power is increased by δ dB. This is done in an iterative manner until the target SINR is reached. At the first transmission, after power control, the SINR is also used in HybridARQ process. Its availability makes it easy to predict the BLER which indicates if a packet is correctly received or erroneous. If a packet is detected to be in error, its retransmission is requested, and the channel matrix and the filter coefficients are saved for Chase combining SINR computation.
Let us consider Chase combining scenario with N_{ t }transmissions. The output of HybridARQ combiner may be written as
where z_{ j }is the filter output corresponding to the j th HybridARQ transmission.
If we introduce a vector G_{ j }= w^{H}H_{ j }, Equation (3.6) can be then expressed in the following vector form
Consequently, the global SINR after the despreading is calculated according to this formula
After SINR estimation, the BLER is derived from prestored LUTs which summarize the iterative turbo decoding behavior. These LUTs are constructed using MonteCarlo simulations through an AWGN channel, and are in the form (E_{ c }/N_{0} in dB, BLER). So, it is necessary to compute an effective E_{ c }/N_{0} from the estimated SINR. For this, we assume that the SINR at the output of the receiver filter is equivalent to the received E_{ b }/N_{0}. Thus, the effective E_{ c }/N_{0} can be calculated as follows
The BLER is then obtained according to this expression
where f(.) indicates the linear interpolation function which takes the effective ${\left(\frac{{E}_{c}}{{N}_{0}}\right)}_{dB}$ as input and calculates the corresponding BLER by linear interpolation using AWGN LUT [8].
4. Effect of channel estimation on the performance prediction
When perfect channel knowledgement is not considered, the receiver has to make channel estimation which cannot be done without error. This affects the performance prediction methodology since the SINR estimation is based on the estimated channel coefficients. In this study, channel estimation is performed by extracting the channel state information contained in the known pilot symbols carried by the UMTS control channel DPCCH [9, 10]. So, DPCCH despreading is necessary for channel estimation. As in [11], under the hypothesis that the channel estimation error has a Gaussian behavior, we can express the estimated channel vector, $\widehat{\mathbf{h}}=\left[{\u0125}_{0},\dots ,{\u0125}_{L1}\right]$, as
where u is a vector of L AWGN samples, each of zero mean, and a variance expressed (see Appendix 1) as
The SINR at the Rake receiver output is given by
If we denote by $\widehat{\mathbf{H}}$ the estimated channel matrix, the vector of LMMSE filter coefficients is written as
Define the vector $\widehat{\mathbf{G}}={\widehat{\mathbf{w}}}_{\mathsf{\text{LMMSE}}}^{H}\mathbf{H}$. The SINR at the output of the LMMSE filter is given by
Performance prediction process with channel estimation is accomplished according to the following steps.

Step 1. Draw a channel realization h,

Step 2. Generate the noisy channel, $\widehat{\mathbf{h}}$ such as $\widehat{\mathbf{h}}=\mathbf{h}+\mathbf{u}$, where u is the vector of AWGN noise samples where each has zero mean and the variance expressed in (4.2),

Step 3. Compute $\mathsf{\text{SINR}}\left(\widehat{\mathbf{h}}/\mathbf{h}\right)$ using the formula in (4.3) for the Rake receiver, or (4.5) for the LMMSE detector,

Step 4. Calculate the mathematical expectation of $\mathsf{\text{SINR}}\left(\widehat{\mathbf{h}}/\mathbf{h}\right)$,

Step 5. Perform the TPC process

Compare $\mathsf{\text{SINR}}\left(\widehat{\mathbf{h}}/\mathbf{h}\right)$ to the target SINR, SINR_{ T }

Update the transmitted chip energy, E _{ c }, using TPC command as expressed in (4.6)

Calculate the $\mathsf{\text{SINR}}\left(\widehat{\mathbf{h}}/\mathbf{h}\right)$ with updated E _{ c }


Step 6. Predict the BLER using LUTs and the final SINR corresponding to current transmission.
Note that TPC process (Step 5) is repeated until the estimated $\mathsf{\text{SINR}}\left(\widehat{\mathbf{h}}/\mathbf{h}\right)$ is close to target SINR. Then, the final value of SINR is used for HybridARQ Chase combining purpose.
To see whether a retransmission of a packet is required or not, the predicted BLER in Step 6 is exploited and compared to a uniform random variable ϑ, in the following criterion
5. Numerical results
In this section, simulation results are presented to investigate the effect of channel estimation on the FPP technique. First, we describe how the LUTs are built. The FPP is then validated with the HSUPA link simulator (LS) according to 3GPP technical specifications [12]. The joint application of Chase combining and transmit power control is also addressed. Finally, simulation results of FPP with channel estimation are presented for HSUPA configurations presented in Table 1.
5.1. Lookup tables
As mentioned above, the LUTs are built using the HSUPA LS with an AWGN channel. The MaxLogMAP algorithm, with 8 iterations, is considered for decoding the 1/3 rate Turbo encoder. The LUTs summarize the HSUPA decoder behavior in terms of BLER as a function of received E_{ c }/N_{0}. Each selected HSUPA modulation and coding scheme (MCS) needs only one LUT, also called a reference curve, for its performance prediction in a multipath channel.
5.2. Fast performance prediction validation
To see how accurate the FPP simulator is, computer simulations are done for HSUPA transmission. The FPP performance are obtained using the simulator corresponding to the structure presented in Figure 3 which consider hybridARQ Chase combining [13, 14]. The TPC process is assumed to be inactive. For FPP verification, it is necessary to run the HSUPA link simulator and FPP simulator with the same assumptions. In the LS, the turbo decoding is performed using the same algorithm and number of iterations as those used for building the LUTs. However, in the FPP simulator, turbo decoding process is not run since its performance is summarized in LUTs.
Simulations were done for MCS 2 through a multipath channel which has the same profile as ITUPedestrian B Channel with the mobile speed of 3 Km/h. Note that profile is used for all computer simulations in this work. Results of FPP and LS simulator, are shown in Figure 4. It is observed that the FPP simulator is accurate compared to the LS for both single transmission and HybridARQ retransmissions. Moreover, with FPP, a significant gain, in terms of CPU time is obtained.
5.3. Channel estimation effect on FPP
Having shown the accuracy of FPP simulator, computer simulations are run to assess the impact of noisy channel on this simulator considering HybridARQ Chase combining and TPC. The HSUPA configuration we adopted in this assessment includes MCS 1 and MCS 3. The number of HybridARQ transmissions is fixed to 2. The detection is done with a Rake receiver for the first configuration. The second one (MCS 3) is detected with LMMSE equalizer. As mentioned in 3GPP technical specifications [9], the maximum number of pilot symbols, N_{pilot}, is fixed to 150 for MCS 1 and 30 for MCS 3.
BLER results of MCS 1 are presented in Figure 5. It is seen that for received E_{ c }/N_{0} lower than 19 dB, no enhancement is provided by the TPC process when perfect channel knowledgement is assumed at the receiver end. However, when received E_{ c }/N_{0} is up to 19 dB, an important gain is offered. In fact, we observed a gap of 2.5dB when a BLER of 10^{2} is reached. If channel estimation is taken into consideration, this gain depends on the number of pilot symbols used to estimate the channel coefficients. A great number of pilots results in more accurate estimated channel coefficients. It is also seen that the noisy channel effect is considerable for a small number of pilot symbols. This effect is significantly reduced after applying TPC.
Figure 6 presents the BLER results of MCS 3 which uses LMMSE equalizer. It is concluded that the system exhibits poor performance for lowest number of pilot symbols.
The channel estimation effect, in terms of transmitted pilot symbols, may be seen using the empirical cumulative density function (CDF) of parameter Delta which is defined to be the difference between the SINR computed assuming perfect channel knowledge and that calculated with noisy channel coefficients. Figure 7 shows the CDF of Delta for different numbers of pilot symbols. It is also seen, in terms of CDF that the impact of channel estimation on fast simulator is significantly reduced when increasing the number of pilot symbols.
6. Conclusion
In this article, we have studied the effect of channel estimation on the simplified simulation methodology for HSUPA. Transmit power control and HybridARQ Chase combining have been considered. It has been seen that a noisy channel effect depends on the number of pilot symbols transmitted on the HSUPA control channel. Simulation results have demonstrated that when a sufficient number of these symbols is used, the performance is close to that obtained with the perfect CHACK assumption. Moreover, the application of TPC compensates the performance degradation when a small number of pilot symbols. This has been verified for both Rake and LMMSE receivers.
Appendix 1: DPCCH despreading
For channel coefficients estimation in HSUPA, we have to extract the time multiplexed control channel, DPCCH, from received signal containing the pilot symbols. For this, we consider the HSUPA transmission scenario presented in Figure 1. The multicode transmitted signal with time multiplexed control channels is expressed as follows
After time multiplexing, the multicode signal is sent through a multipath channel which has the well known impulse response given by this expression
The received signal is then expressed as
DPCCH despreading is performed by calculating the intercorrelation between the received multicode signal and the spreading code corresponding to DPCCH channel. The output of the despreading process is written as
where n_{ l }(t) is the noise after despreading. The l th channel coefficient is estimated by multiplying the despreading output by the transmitted pilot sequence. This is expressed as
Since the additive noise is an AWGN with zero mean (by simulation), its effect on channel estimation can be significantly reduced by taking an average of ${\stackrel{\u0303}{h}}_{l}$ over the pilot sequence size, N_{ p }.
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Many thanks to our TEXpert for developing this class file.
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Ettolba, M., Saoudi, S. & Visoz, R. Fast performance prediction of power controlled HSUPA with channel estimation. J Wireless Com Network 2012, 148 (2012) doi:10.1186/168714992012148
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Keywords
 HSUPA
 HybridARQ
 chase combining
 TPC
 LMMSE
 channel estimation
 pilot symbols