Publication Type : Conference Paper
Publisher : Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies,
Source : Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2017, Institute of Electrical and Electronics Engineers Inc. (2017)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037364840&doi=10.1109%2fICCPCT.2017.8074354&partnerID=40&md5=c136b0ce3c232ac29a78b6dd7ff500e0
ISBN : 9781509049660
Keywords : Bit error rate, Channel estimation, Compressed sensing, Compressive sensing, Computer circuits, Doppler sparse, Errors, Least Square, Least square channel estimations, Least-square techniques, Mean square error, Millimeter waves, MIMO systems, Minimum mean square error estimations, Orthogonal matching pursuit, Reconstruction algorithms
Campus : Coimbatore
School : Department of Electronics and Communication Engineering
Department : Electronics and Communication
Year : 2017
Abstract : Millimeter waves (MMW) are meant for high data rate short range indoor communications. The indoor environment is Doppler sparse, due to slow movement of objects and humans. Thus the Doppler spread becomes negligible. Owing to this fact channel estimation using compressive sensing is used. Conventionally, training symbol based linear channel estimation techniques least squares, minimum mean square error estimation is used in the multiantenna setup. The linear techniques work with the linear combination of multipath symbols and hence computationally complex. Hence in this work, compressive sensing and least square channel estimation techniques are compared with respect to the bit error rate. Comparative analysis indicate compressive sensing achieves the same performance as least square technique with respect to bit error rate however with reduced number of samples. In compressive sensing based channel estimation, Orthogonal Matching Pursuit (OMP) is used as the reconstruction algorithm due to its fast convergence rate and reduced computational complexity.IEEE.
Cite this Research Publication : S. Kirthiga, “Compressive sensing based channel estimation for millimeter wave MIMO”, in Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2017, 2017.