The performance analysis of B-WLL system using pre-equalization techniques with fast adaptive algorithms under the Ka-Band channel

In this paper, we propose the pre-equalization technique for the uplink burst transmission under the intersymbol interference (ISI) channel and the rain attenuation channel existed in the Ka-Band (20~30GHz) of the B-WLL system. We compare the mean-square-error (MSE) convergence properties of standard leastmean-square (SLMS) and discrete cosine transform (DCT)-based transform-domain least-mean-square (TRLMS) algorithms, analyze the BER performance of pre-equalization and post-equalization using TRLMS and SLMS algorithms. Pre-equalization, which is a technique to enhance the BER performance by avoiding the noise enhancement of receiver, needs the post-equalization as an initial step to extract the tap coefficients of pre-equalizer in the transmitter. The simulation results show that TRLMS as a post-equalization algorithm provides much faster MSE convergence rate and lower steady-state MSE properties than SLMS does under the ISI and rain attenuation channel. From the above MSE results, it is preferable to use the TRLMS as an adaptive filter algorithm of pre-equalizer system rather than the SLMS to obtain better BER performance. Key-Words: ISI, SLMS, MSE, TRLMS, DCT, Equalization, Ka-Band The statistical channel model of B-WLL system that is usually performed in the LOS communication is similar to the Ka band fixed orbit satellite channel model. This channel model can be easily influenced by the rain attenuation channel that be expressed as Gaussian function with different parameters. The channel condition of B-WLL system is good because B-WLL system requires the LOS component to provide high quality service. But distortions occur to signals that pass through the multipath channel and the rain attenuation channel. Computer simulations were performed to compare the MSE convergence performance of SLMS and TRLMS adaptive filter algorithms, to analyze the BER performance of post-equalization and pre-equalization techniques using two adaptive filter algorithms under the ISI and the rain attenuation channels. Simulation results show that the TRLMS algorithm provides much faster and lower MSE convergence properties than SLMS algorithm regardless of channel conditions. These also show the improved BER performance by using of the pre-equalization technique. In Section 2,3,4, LMS adaptive algorithm, Rain Attenuation channel and Pre-equalization are reviewed. In Section 5, performance comparison is performed. Conclusions are drawn in Section 6. 2 LMS adaptive algorithm 2.1 SLMS (Standard LMS) SLMS tap coefficient update algorithm, which is defined as ) ( ) ( ) ( ) 1 ( * n n e n n k k X c c ⋅ ⋅ + = + μ (1) where μ is step-size parameter, ) (n e is error signal, and ) (n X is input signal.