MC CDMA is a thriving topic in recent years. Multiuser interference is also very severe as in DS CDMA. ML method is the best multiuser detection, but it has a computational complexity exponentially increased with the number of users. Mean field annealing and chaotic neural network are two promising optimum techniques. This paper applies them into the ML detection, comparison of the two methods is made.
This paper describes a linear interference cancellation multi user detector for synchronous DS CDMA systems under the condition that all spread spectrum code waveforms have the constant cross correlating coefficients. The basic idea is to get the estimation for total multiple access interference (MAI) of all users using a reference code waveform, then subtract the total MAI from the received signal. The structure of such a detector is nearly similar to the conventional detector. The BER expression obtained in the paper shows significant performance improvement compared to the other detectors.
