 Research
 Open Access
Fast performance prediction of power controlled HSUPA with channel estimation
 Mohamed Ettolba^{1}Email author,
 Samir Saoudi^{2} and
 Raphaël Visoz^{3}
https://doi.org/10.1186/168714992012148
© Ettolba et al; licensee Springer. 2012
Received: 2 June 2011
Accepted: 19 April 2012
Published: 19 April 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).
Keywords
 HSUPA
 HybridARQ
 chase combining
 TPC
 LMMSE
 channel estimation
 pilot symbols
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
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
${h}_{l}^{i}\left(0\le l\le L1\right)$ 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
3.2. SINR estimation
where H_{E+L}is the (E + L)th column of channel matrix H.
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.
where z_{ j }is the filter output corresponding to the j th HybridARQ transmission.
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
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.
5. Numerical results
HSUPA modulation and coding schemes
MCS  Number of codes  Min. SF  Coding rate  TTI (ms)  Max. bit rate (kbps) 

1  1  16  0.288  10  69.0 
2  2  2  0.502  10  1927.0 
3  2  2  0.702  2  2706.0 
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.
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.
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
Declarations
Acknowledgements
Many thanks to our TEXpert for developing this class file.
Authors’ Affiliations
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