Proceedings of the International Conference on Computer and Communication Engineering 2008 May 13-15, 2008 Kuala Lumpur, Malaysia

978-1-4244-1692-9/08/$25.00 ©2008 IEEE

Downlink Channel Estimation and Tracking in Mobile WiMAX Systems

Masrul Faizal Mohamad1, Mohammed Abdo Saeed1, Akhmad Unggul Priantoro2 1Wireless Broadband, MIMOS Berhad, Technology Park Malaysia, Kuala Lumpur

2Department of Electrical and Computer Engineering, Faculty of Engineering, International Islamic University Malaysia.

{masrul.mohamad, m.saeed}@mimos.my, unggul@iiu.edu.my

Abstract

Mobile WiMAX is a new promising technology based on IEEE 802.16-2005 that supports high-speed, broadband fixed and mobile services through using orthogonal frequency division multiple access (OFDMA). In mobile communications, channel estimation is important for system performance, but at the same time it is very crucial when the fading channel is fast time-varying. In this paper, a cluster-based downlink channel estimation and tracking in mobile WiMAX is evaluated based on least square (LS) methods. Interpolation between channel estimates at pilots is carried out both over frequency and time to compensate between high data transfer rate as well as efficient channel estimation. Simulation results in terms of mean square error (MSE) and bit error rate (BER) show that channel estimation over two OFDM symbols exhibits better performance compared to that of single OFDM symbol even at high Doppler frequencies.

I. INTRODUCTION Wireless communications has permeated nearly all

facets of human life e.g. home, office, car etc. with the future goal being broadband access and services being available seamlessly virtually everywhere. From a user perspective, this trend of increased use of wireless technology is going to continue because of the convenience, flexibility, and enhanced productivity they offer. From a technical perspective, the trend is towards higher and higher data rates with continued need for higher quality of services. Fourth generation (4G) systems are envisioned to support high mobility and bit rates greater than 5 Mbits/sec and can reach up to 155 Mbits/sec [1].

One of the candidates for 4G systems is Worldwide Interoperability for Microwaves Access (WiMAX) which is based upon IEEE 802.16 standard for Wireless Metropolitan Area Network (WMAN) [2]. In 2004, the IEEE 802.16d standard [3] was published for

Fixed Wireless Access (FWA) applications. In December 2005 the IEEE ratified the 802.16e [4] amendment, which aimed to support Mobile Wireless Access (MWA) with seamless network coverage. This standard is now receiving considerable industrial attention. Mobile WiMAX is based on orthogonal frequency division multiplexing (OFDM) technology. OFDM is a transmission technique that is built-up by many orthogonal carriers that transmits simultaneously. The main idea behind OFDM is that a signal with a long symbol duration time is less sensitive to multipath fading, than a signal with a short symbol time. Hence, a gain in performance can be achieved by sending several parallel symbols with a long symbol time than sending them in a series with a shorter symbol time. Thus this reduces the effect of inter-symbol interference (ISI) and the remaining ISI effect is eliminated by cyclically extending the signal [5]. Generally OFDM is used for Fixed WiMAX and orthogonal frequency division multiple access (OFDMA) is for Mobile WiMAX. OFDMA is a multi-user version of OFDM, and all that were previously mentioned about OFDM also holds for OFDMA. Mobile WiMAX uses Scalable Orthogonal Frequency Division Multiple Access (SOFDMA) as transmission technique. SOFDMA is an OFDMA version where the bandwidth is scalable; in 802.16e it is scalable 1.25 to 20 MHz. The scalability is achieved by changing the FFT size, while keeping fixed subcarriers spacing [6].

To realize the capability of WiMAX systems, receiver algorithms play a very important role. In this paper, channel estimation, which is one of the key blocks in WiMAX receivers as well as one of the most important elements of wireless receivers that employs coherent demodulation, is studied. Generally, channel estimation is a challenging problem in wireless systems. Unlike other guided media, the radio channel is highly dynamic. The transmitted signal travels to the receiver by undergoing detrimental effects that corrupt the signal and often place limitations on the performance of the system. Transmitted signals are typically reflected and scattered, arriving at the

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receivers along multiple paths. Also, due to the mobility of transmitters, receivers, or scattering objects, the channel response can change rapidly over time. Most important of all, the radio channel is highly random and the statistical characteristics of the channel are environment dependent. Multipath propagation, mobility, and local scattering cause the signal to be spread in frequency, time and angle. These spreads, which are related to the selectivity of the channel, have significant implications on the received signal. Channel estimation performance is directly related to these statistics. Different techniques are proposed to exploit these statistics for better channel estimates, especially for OFDM based systems. Although there are many channel estimation techniques for OFDM-based system reported in the literature [7], for practical WiMAX systems it is important to have an estimation technique that is specifically designed for WiMAX preambles or pilots, and has low computational and hardware complexities. This is because in Mobile WiMAX the pilots are not equidistantly distributed and the number of pilots per OFDMA symbol does not follow the Nyquist rate which makes channel estimation in WiMAX challenging. In this paper, cluster-based channel estimation in Mobile WiMAX is considered.

The remainder of this paper is organized as follows. In section II, system model will be introduced. Section III explains the cluster-based channel estimation and tracking method. Simulation and results are presented in Section IV while conclusions are drawn in Section V.

II. SYSTEM MODEL The OFDM concept utilizes parsing of the serial

input data bit stream into N symbol streams and each of which in turn is used to modulate parallel, synchronous subcarriers. These parallel blocks are modulated using inverse fast Fourier transform (IFFT). Time domain samples of an OFDM symbol can be obtained from frequency domain data symbols as

( ){ }

( ) 10

)(2/

02/

/2 N-nekX

kXIFFTnxused

used

FFT

N

kN

Nnkj ≤≤=

=

∑≠

−

π (1)

where X(k) is the transmitted data symbol at the kth subcarriers of the OFDM symbol, NFFT is the fast Fourier transform (FFT) size and Nused is the number of nonsuppressed subcarriers. In the frequency domain, each OFDM symbol is created by mapping the sequence of symbols on the subcarriers. Mobile

WiMAX has three classes of subcarriers [8]. They are data subcarriers which are used for carrying data symbols, pilot subcarriers which are used for carrying pilot symbols and null subcarriers which have no power allocated to them, including DC subcarriers and guard subcarriers toward the edge. The pilot

Figure 1. Frequency domain representation of OFDM symbol

symbols are known a priori and can be used for channel estimation and channel tracking. The typical frequency domain description of an IEEE802.16e-2005 OFDM symbol is shown in Figure 1 below.

After the addition of cyclic prefix (CP) and D/A conversion, the signal is passed through the mobile radio channel. The channel impulse response is assumed that the entire impulse response lies in between the guard time, TG.

At the receiver, the signal is received along with noise. After synchronization and removing the CP, the simplified baseband model of the received samples is

( ) ( ) ( ) ( )∑−

=

+−=1

0

L

l

nwlhlnxny (2)

where L is the number of sample-spaced channel taps, w(n) is the additive white Gaussian noise (AWGN) sample with zero mean and variance of 2

wσ , and the time domain channel impulse response (CIR) for the current OFDM symbol, h(l), is given as a time-invariant linear filter. Note that perfect time and frequency synchronization is assumed. After taking FFT of the received signal y(n), the samples in frequency domain can be written as

( ) ( ){ })()()( kWkHkX

nyFFTkY+=

= (3)

where H and W are FFTs of h and w respectively.

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III. CHANNEL ESTIMATION AND TRACKING METHOD Based on (3), the LS estimate of the channel frequency response (CFR) H can be calculated using the received signal and the knowledge of transmitted symbols as:

( ) ( ) (4) ˆ 1 Y X XXkH H-HLS =

LS method is the simplest channel estimation

method since it can readily be implemented without knowing the channel statistics compared to other more advanced techniques such as minimum mean square error (MMSE) [9]. Even though MMSE method are more robust against noise and perform better than LS estimators, its dependence on channel statistics and the operating signal to noise ratio (SNR) makes it disadvantageous.

In this paper, channel estimation method is based on clusters. In order to even effectively mitigate the effect of fading, the IEEE 802.16e standard has a very unique feature in its subcarrier allocation method. This feature is called permutation and there are a few types of permutations. In this paper we are going to use a method called Downlink Partially Used Subchannelization (DL-PUSC) [6]. In DL-PUSC, the subcarriers are divided into clusters containing 14 adjacent subcarriers each. Figure 2 shows a cluster structure and the position of pilot subcarriers in each cluster for even and odd symbols.

Figure 2. Cluster structure for DL PUSC

Each OFDMA symbol is divided into physical

clusters, each containing 14 adjacent subcarriers in which 2 of them are allocated for pilots. These physical clusters are renumbered into logical clusters. The logical clusters are divided into 6 subchannel major groups which can be allocated to one or more of the 3 segments of the downlink subframe. Then, a user in a segment needs to extract the information only in the clusters allocated to him. However, the clusters allocated to a user are usually not adjacent to each other and the spaces between them are not regular, which results in highly unequally-spaced pilot tones. Therefore, we ignore the influence between clusters of an OFDMA symbol and carry out channel estimation per cluster.

Referring to Figure 3, channel coefficients at pilot subcarriers (black) are estimated by using (4), while

channel coefficients at data subcarriers (gray) are estimated through linear interpolation in time domain. Then, we carry out interpolation in frequency domain over each cluster to estimate the channel coefficients at remaining data subcarriers (white).

Figure 3. Pilot allocations in successive clusters

IV. SIMULATIONS AND RESULTS In this simulation we set the parameters as in Table

1. It is assumed that the channel is constant over an OFDM symbol, but time-varying within an OFDMA frame, which is a reasonable assumption for low and medium mobility.

TABLE 1: SIMULATION PARAMETERS

Parameters Value FFT size (NFFT) 1024 System Bandwidth (MHz) 10 Sampling frequency (MHz) 11.2 Channel Coding Convolutional

Coding (CC) Code rate ¾ Modulation 16-QAM Number of Guard Subcarriers 91+92 Number of Used Subcarriers (Nused) including all possible allocated pilots and DC subcarrier.

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Number of subcarriers per cluster

14

Number of clusters used 24 Cyclic Prefix ratio 1/8 OFDM symbol duration (µs) 102.86 Fading channel Rayleigh Number of channel tap 8 Carrier Frequency (GHz) 3.5

The estimation performance is measured in terms of Mean Square Error (MSE) and system Bit Error Rate (BER). For comparison purposes, the BER performance over ideal channel i.e. the channel is perfectly known to the receiver is also included. The

EvenOdd

Pilot subcarrier Data subcarrier

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performance is presented for different Doppler frequencies which correspond to pedestrian velocity of 6 km/hr, mobile speeds of 60 km/hr and 120 km/hr.

In Figure 4, the MSE versus Signal-to-Noise Ratio (SNR) is plotted. As the Doppler frequency is getting higher, the error is getting less. This is due to some part of the frame which is in bad condition can be corrected by the Viterbi decoder based on some part of the frame which is in good condition since the channel is slowly time varying.

In Figure 5, the BER versus SNR is plotted. The ideal channel is used as a reference. It is noted that as increasing Doppler frequency at low SNR has no significant effect on the BER performance. It is observed that at BER=10-2, 3 dB difference in the performance between the ideal and estimated channel case.

10 12 14 16 18 20 22 24 26 28-38

-36

-34

-32

-30

-28

-26

-24

-22

-20

10log10(ES/No)

MS

E (d

B)

fD = 20 Hz

fD = 200 Hz

fD = 390 Hz

Figure 4. Mean Square Error (MSE) for different doppler frequencies, fD.

10 12 14 16 18 20 22 24 26 2810-6

10-5

10-4

10-3

10-2

10-1

100

10log10(ES/No)

BE

R

fD = 20 Hz

fD = 200 Hz

fD = 390 Hz

Ideal channel

Estimated channel

Figure 5. Bit Error Rate (BER) for different doppler frequencies,

fD.

V. CONCLUSIONS In this paper, channel estimation in Mobile WiMAX

is investigated and analyzed based on clusters, which solves some tough questions brought by mobile channel and pilot distribution scheme of IEEE 802.16e. Both analysis and simulation results have demonstrated its sound performance with relatively low complexity.

ACKNOWLEDGMENT The authors would like to thank Dr. Essam Sourour

from Alexandria University, Egypt and Dr. Masoud Alghoniemy from MIMOS Berhad for their valuable technical inputs.

REFERENCES [1] S. Y. Hui and K. H. Yeung, “Challenges in the Migration to 4G

Mobile Systems,” IEEE Communications Magazine, Vol. 41, Issue 12, Dec 2003, pp. 54-59.

[2] C. Eklund, R. B. Marks, K. L. Stanwood and S. Wang, “IEEE Standard 802.16: A Technical Overview of the WirelessMAN™ Air Interface for Broadband Wireless Access,” IEEE Communications Magazine, Vol. 40, Issue 6, June 2002, pp. 98-107.

[3] IEEE 802.16-2004, “Part 16: Air interface for fixed broadband wireless access systems,” Oct. 2004.

[4] IEEE 802.16e-2005, “Part 16: Air interface for fixed and mobile broadband wireless access systems,” Feb. 2006.

[5] R. Prasad and R. Van Nee, OFDM For Wireless Multimedia Communications, Artech House Publisher, June 2000.

[6] H. Yaghoobi, “Scalable OFDMA Physical Layer in IEEE 802.16 WirelessMAN,” Intel Technology Journal, Vol. 8, Issue 3, Aug. 2004, pp. 201-212.

[7] M. K. Ozdemir and H. Arslan, “Channel estimation for wireless OFDM systems,” IEEE Communications Survey & Tutorials, 2nd Quarter 2007, Vol. 9, Nov. 2, pp. 18-48.

[8] J. Andrews, A. Ghosh, and R. Muhamed, Fundamentals of WiMAX: Understanding Broadband Wireless Networking, Prentice Hall, Feb. 2007.

[9] S. Wu and Y. Bar-Ness, “OFDM Channel Estimation in the presence of Frequency Offset and Phase Noise,” IEEE ICC’03, Vol. 5, pp. 3366-3370.

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