Analysis of Adaptive Direct sequence Spread Spectrum using Least mean squares Algorithm

Spread spectrum forms an important aspect of digital communication technology where the message signal is transmitted over a channel having bandwidth much greater than required. Direct Sequence Spread Spectrum (DSSS) belongs to the category of spread spectrum methods and forms an important part in mobile communications. The objective of this paper is to investigate the FIR optimal, Wiener and least mean square (LMS) algorithms in the design of traversal tap delay line filters for the purpose of compensating the effect of the quasi-static communication channel in mobile communication using DSSS. The designed equalizers remove the distortion caused by the channel from the transmitted signal with the help of an adaptive, recursive Weiner filtering technique using the concept of Weiner-Hopf equation using LMS algorithm and to aid in the equalization process, a training sequence is first transmitted to adapt the filter coefficients. The performance metrics are studied for DSSS system with and without adaptive equalization. Further, the convergence of LMS algorithm is also verified as well as the impact of different step-sizes on the speed of the conversion and the accuracy of the overall algorithm. Exhaustive Monte Carlo simulative analysis have been performed to investigate the scheme under varying distortion levels and Signal to noise ratio (SNR) values via impulse response, frequency response and uncoded Bit Error Rate