Two-Dimensional Set Membership Normalized Least Mean Square Adaptive Channel Estimation for OFDM Systems

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(N²) while compromising on the convergence speed.