Proceedings ArticleDOI
Two-Dimensional Set Membership Normalized Least Mean Square Adaptive Channel Estimation for OFDM Systems
Tanvi Sharma,Akshay Soni,Vijay Kumar Chakka +2 more
- pp 1-4
TLDR
A Set Member Feasibility (SMF) formulation to govern the updating of the adaptive-filter coefficients of Orthogonal Frequency Division Multiplexing and two- dimensional Set-Membership Normalized LMS (2D-SM-NLMS) algorithm is proposed.Abstract:
Orthogonal Frequency Division Multiplexing (OFDM) is seen as one of the most promising solution to broadband wireless communications. Its performance depends on the channel state information (CSI) which can be estimated using different channel estimation algorithms. This paper proposes a Set Member Feasibility (SMF) formulation to govern the updating of the adaptive-filter coefficients. Here, two- dimensional Set-Membership Normalized LMS (2D-SM-NLMS) algorithm is proposed. The 2D-RLS adaptive channel estimation algorithm is also simulated for comparison. Matlab simulations show that 2D-SM-NLMS and 2D-RLS algorithms have similar Bit Error Rate (BER) performance. But the proposed algorithm has computational complexity of O(N) which is less than that of conventional 2D-RLS algorithm having order O(N 2 ) whileread more
Citations
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Journal ArticleDOI
Inverse QR 2D-RLS Adaptive Channel Estimation for OFDM Systems
TL;DR: This letter proposes Inverse QR two-dimensional Recursive Least Square adaptive channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) systems (using Givens Rotations and Householder Transformations) that is more stable numerically than 2D-RLS algorithm.
References
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Adaptive Filtering: Algorithms and Practical Implementation
TL;DR: Adaptive Filtering: Algorithms and Practical Implementation may be used as the principle text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.
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Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size
TL;DR: A recursive solution for set membership filtering is derived that resembles a variable step size normalized least mean squares (NLMS) algorithm and shows significant performance improvement in varied environments with a greatly reduced number of updates.
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