MIMO-OFDM Channel Tracking Using a Dynamic ANN Topology

Authors: Manasjyoti Bhuyan, Kandarpa Kumar Sarma

Abstract:

All the available algorithms for blind estimation namely constant modulus algorithm (CMA), Decision-Directed Algorithm (DDA/DFE) suffer from the problem of convergence to local minima. Also, if the channel drifts considerably, any DDA looses track of the channel. So, their usage is limited in varying channel conditions. The primary limitation in such cases is the requirement of certain overhead bits in the transmit framework which leads to wasteful use of the bandwidth. Also such arrangements fail to use channel state information (CSI) which is an important aid in improving the quality of reception. In this work, the main objective is to reduce the overhead imposed by the pilot symbols, which in effect reduces the system throughput. Also we formulate an arrangement based on certain dynamic Artificial Neural Network (ANN) topologies which not only contributes towards the lowering of the overhead but also facilitates the use of the CSI. A 2×2 Multiple Input Multiple Output (MIMO) system is simulated and the performance variation with different channel estimation schemes are evaluated. A new semi blind approach based on dynamic ANN is proposed for channel tracking in varying channel conditions and the performance is compared with perfectly known CSI and least square (LS) based estimation.

Keywords: MIMO, Artificial Neural Network (ANN), CMA, LS, CSI.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332270

References:

[1] H. Bolcskei and E. Zurich, MIMO - OFDMA Wireless Systems: Basics, Perspectives, and Challenges. IEEE Wireless Communications, August, 2006, pp. 31-37.
[2] M. Jiang and L. Hanzo, "Multiuser MIMO-OFDM for Next-Generation Wireless Systems," Proceedings of the IEEE, vol. 95, no. 7, pp.1430- 1469, July 2007.
[3] K. J. Kim, T. Reid and R. A. Iltis, "Data Detection and Soft-Kalman Filter based Semi-Blind Channel Estimation Algorithms for MIMO-OFDM Systems", IEEE 0-7803-8938-7, 2005.
[4] B. B. Baruah and K. K. Sarma, ANN Based Equalization using Coded Inputs in Nakagami-m Faded Channel, International Journal of Smart Sensors and Ad Hoc Networks (IJSSAN), vol. 1, issue 3, pp. 83-86, 2012.
[5] S. Colieri, M. Ergen, A. Puri and A. Bahai, "A Study of Channel Estimation in OFDM Systems", In Proceedings of the 56th IEEE Vehicular Technology Conference, vol. 2, pp.894-898, 2002.
[6] G. Kechriotis, E. Zervas, and E. S. Manolakos, "Using Recurrent Neural Networks for Adaptive Communication Channel Equalization", IEEE Transactions on Neural Networks, vol. 5. no. 2, 1994.
[7] K. K. Sarma and A. Mitra, Modeling MIMO Channels using a Class of Complex Recurrent Neural Network Architectures, Elsavier International Journal of Electronics and Communications, vol.- 66, Issue 4, pp. 322331, April, 2012.
[8] P. Gogoi and K. K. Sarma, Pilot Assisted Channel Estimation Technique for Alamouti STBC- MISO and MIMO Set-up with BPSK and QPSK modulation, in Proceedings of IEEE National Conference on Emerging Trends and Applications in Computer Science - 2012 (NCETACS - 2012), pp. 246-252, Shillong, India, March, 2012.
[9] P. Gogoi and K. K. Sarma, Kalman Filter and Recurrent Neural Network based Hybrid Approach for Space Time Block Coded-MIMO Set-up with Rayleigh Fading, in Proceedings of 13th WSEAS International Conference on Neural Netwroks (NN 12), Iasi, Romania, pp. 41-46, 2012.
[10] P. Gogoi and K. K. Sarma, Channel Estimation Technique for STBC coded MIMO System with Multiple ANN Blocks, International Journal of Computer Applications, vol. 50, no. 13, pp. 10-14, July, 2012.
[11] K. K. Sarma and A. Mitra, Estimation of MIMO Wireless Channels using Artificial Neural Networks , Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, IGI Global, USA, pp. 509-545, 2011.
[12] K. K. Sarma and A. Mitra, MIMO Channel Modelling with Cluster Con- figuration of Complex Time Delay Fully Recurrent Neural Network, in Proceedings of IEEE Recent Advances in Intelligent Computational Systems (RAICS 2011), Trivundrum, India, September, 2011.
[13] S. Haykin, ”Neural Networks A Comprehensive Foundation”, 2nd ed., Pearson Education, New Delhi, 2003.