Code Division Multiple Access (CDMA) system is being most popular communication technique due to its robustness & spread spectrum techniques. The CDMA include the pseudo sequences (PN-sequences) which provide the secure communication over a wide band. But the Multiple Access Interference (MAI) limits the capacity of Direct Sequence Code Division Multiple Access (DS-CDMA) based systems. For DS-CDMA systems, MAI is behaves as additive noise and a matched filter bank is employed and due to superposition of MAI with noise the capacity of the system reduced. Traditionally, multiuser detectors a code-matched and a multiuser linear filter are employed which increases the complexity of the system due to its operation methodology.

The Multiuser Detection (MUD) is an approach which uses both these filters for the optimization. However, the limitation at implementation-end of the optimal multiuser detection is its processing complexity and processing delay. Recent research in communication system with MUD is concentrate to find an appropriate trade off between complexity and performance. These suboptimal techniques are further sub-divided into linear and non-linear algorithms.

In this dissertation, we implement Groupwise Successive Interference Cancellation (GSIC) and Successive Interference Cancellation (SIC). Both Interference Cancellation techniques are nonlinear suboptimal methods of MUD and are based upon successively removing a fraction which results in optimal Interference. Further analysis and rigorous study is to be carried out and extensive simulations are done for better understanding of GSIC and SIC techniques.

There are several schemes for the performance evaluation of a CDMA scheme which omits the Interference Cancellation, the main analysis is to be done is SIC & improvement in SIC using Multi User Group Wise Detection & Interference Cancellation (GSIC). The rigorous simulation work is being carried out to resolve the Multi User Detection for CDMA system; a novel approach can be derived for Interference Cancellation using Successive subtractions & GroupWise estimation for better results.