M. NASSERI ET AL.

387

Figure 3. MSE performance of channel estimation compar-

ing with ideal channel.

estimation improvement, so that the system works in

lower Signal to Noise Ratio (SNR) by using advantages

of PCA method. Therefore it is unnecessary to use com-

plex algorithms to enhance estimation quality. Addition-

ally, the benefits of MIMO systems are manipulated by-

contributing all channels in estimation.

5. Conclusions

Acquiring accurate information at receiver depends on

channel estimation quality. Therefore in this paper,

channel estimation improvement was considered in a 2 ×

2 MIMO-OFDM system. This kind of system gets ad-

vantages of both MIMO and OFDM techniques. It has

high channel capacity and also ISI and Inter-Channel

Interference (ICI) are removed due to converting fre-

quency selective fading channel to flat fading subchan-

nels. In channel estimation section, channel was esti-

mated initially by LS method, using training sequences

which were sent in some subcarriers with equal distances

emerge of sampling theory. After evaluating channel in

pilot subcarriers, linear interpolator was used to estimate

channel in all subcarriers. In the final step, to improve

channel estimation, PCA method was chosen to project

data onto directions of eigenvalues and reduced complex

data to lower dimension. Simulation results show im-

provement of channel estimation in BER and MSE per-

formance.

6. References

[1] E. Telatar, “Capacity of Multi-Antenna Gaussian Chan-

nels,” European Transactions on Telecommunication,

Vol. 10, No. 6, 1999, pp. 585-595.

doi:10.1002/ett.4460100604

[2] J. G. Proakis, “Digital Communications,” 4th Edition,

McGraw-Hill, New York, 2001, pp. 709-800.

[3] S. Roy and C. Y. Li, “A Subspace Blind Channel Estima-

tion Method for OFDM Systems without Cyclic Prefix,”

IEEE Transactions on Wireless Communications, Vol. 1,

No. 4, 2002, pp. 572-579.

doi:10.1109/TWC.2002.804160

[4] Y. Li, “Simplified Channel Estimation for OFDM Sys-

tems with Multiple Transmit Antennas,” IEEE Transac-

tions on Wireless Communications, Vol. 1, No. 1, 2002,

pp. 67-75. doi:10.1109/7693.975446

[5] M.-H. Hsieh and C.-H. Wei, “Channel Estimation for

OFDM Systems Based on Comb-Type Pilot Arrangement

in Frequency Selective Fading Channels,” IEEE Transac-

tions on Consumer Electronics, Vol. 44, No. 1, 1998, pp.

217-225. doi:10.1109/30.663750

[6] H. Minn and N. Al-Dhahir, “Optimal Training Signals for

MIMO OFDM Channel Estimation,” IEEE Transactions

on Wireless Communications, Vol. 5, No. 5, 2006, pp.

1158-1168. doi:10.1109/TWC.2006.1633369

[7] S. Haykin, “Neural Networks,” Prentice-Hall, Upper

Saddle River, 1994, pp. 363-370.

[8] M. Nasseri and H. Bakhshi, “Iterative Channel Estima-

tion Algorithm in Multiple Input Multiple Output Or-

thogonal Frequency Division Multiplexing Systems,”

Journal of Computer Science, Vol. 6, No. 2, 2010, pp.

224-228. doi:10.3844/jcssp.2010.224.228

[9] A. Oppenheim a nd R. Sc hafer, “Discre te Time Signal,” 2nd

Edition, Prentice-Hall, Upper Saddle River, 1999, p. 146.

[10] R. V. Nee and R. Prasad, “OFDM for Wireless Multime-

dia Communications,” Artech House, London, 2000, pp.

33-50.

[11] V. Erceg, K. V. S. Hari, M. S. Smith and D. S. Baum,

“Channel Models for Fixed Wireless Applications,” IEEE

802.16.3 Task Group Contributions, 2001.

Copyright © 2011 SciRes. IJCNS