Pilot based MMSE Channel Estimation for Spatial Modulated OFDM Systems
Abstract
Due to the multifold growth in demands of multimedia services and mobile data, the request for increased channel capacity in mobile and wireless communication has been quickly increasing. Developing a wireless system with more spectral efficiency under varying channel condition is a key challenge to provide more bit rates with limited spectrum. Multiple Input Multiple Output(MIMO) system with Orthogonal Frequency Division Multiplexing (OFDM) gives higher gain by using the direct and the reflected signals, thus facilitating the transmission at high data rate. An integration of Spatial Modulation (SM) with OFDM (SM OFDM) is a newly evolved transmission technique and has been suggested as a replacement for MIMO -OFDM transmission. In practical scenarios, channel estimation is significant for detecting transmitted data coherently. This paper proposes pilot based, Minimum Mean Square Error (MMSE) channel estimation for the SM OFDM communication system. We have focused on analyzing Symbol Error Rate (SER) and Mean Square error (MSE) under Rayleigh channel employing International Telecommunication Union (ITU) specified Vehicular model of Pilot based MMSE channel estimator using windowed Discrete Fourier Transform (DFT) and MMSE weighting function. Simulation output shows that proposed estimator’s SER performance lies close to that of the MMSE optimal estimator in minimizing aliasing error and suppressing channel noise by using frequency domain data windowing and time domain weighting function. Usage of the Hanning window eliminates error floor and has a compact side lobe level compared to Hamming window and Rectangular window. Hanning window has a larger MSE at low Signal to Noise Ratio (SNR) values and decreases with high SNR values. It is concluded that data windowing technique can minimize the side lobe level and accordingly minimize channel estimation error when interpolation is done. MMSE weighting suppresses channel noise and improves estimation performance. Since Inverse Discrete Fourier Transform(IDFT)/DFT transforms can be implemented with fast algorithms Inverse Fast Fourier Transform( IFFT)/Fast Fourier Transform(FFT) computational complexity can be remarkably reduced.
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