Complexity Reduction and Performance Improvement of Multistage Detector with Parallel Interference Cancellation for DS-CDMA System

Sedki B T Younis,Bayez K Al-Sulaifanie

doi:10.33899/rengj.2008.44730

Abstract

In this paper we propose a new method which combines the partial cancellation and differencing technique to achieve performance improvement and reduction in complexity, the proposed technique is named Multistage (DP-PIC) Detector. The simulation model for the proposed DP-PIC is implemented in floating and fixed point arithmetic. The simulation results illustrates that a partial cancellation factor of 0.7 and 0.8 in the first and second stage respectively gives a good performance for the proposed technique. A precision of 16-bit is enough to achieve a small performance degradation compared to floating point results. Finally the proposed fixed point DP-PIC is implemented on TMS320C6400 DSP simulator. The implementation results illustrate that 35% complexity reduction can be achieved compared with conventional PIC detection. Keywords: DS-CDMA, PIC, Complexity reduction, performance improvement.

Highlights

Accepted 8 Oct. 2007Wireless communications have become one of the hottest research areas in the world
Increasing the number of Parallel Interference Cancellation (PIC) stages will increase the complexity of the receiver, using 3-stage of PIC will be a good compromise between the performance and complexity [5]
It cancels efficiently multiple access interference (MAI) and has reduced computational complexity. When it is compared with conventional detection technique it has superior performance. When it is implemented in 16-bit fixed point arithmetic it is showed little degradation in performance compared to floating point implementation

Summary

Introduction

Accepted 8 Oct. 2007Wireless communications have become one of the hottest research areas in the world. PIC detectors have multiple stages for interference estimation and cancellation. In the Multistage PIC detector the interference is cancelled from the matched filter outputs or outputs of previous stages by using the estimates of the data symbols as well as the known cross-correlations between users as shown in figure(3). A common way to do this is to use the matched filter outputs or outputs of a previous stage, which are both referred to as soft decisions, as a joint estimation of the detected bits and the received signal amplitudes.

Results

Conclusion