Microcellular radio direct-sequence code division multiple access (DC-CDMA) system using optical link to connect their base stations to a central station is a solution of cost-effective and efficient spectrum reuse to meet the growing demand for mobile communications. In addition to the inherent multiuser interference (MUI) of CDMA signals, the system capacity is significantly reduced by a nonlinear distortion (NLD) due to the nonlinearity of optical link. In this paper, a two-stage cancellation technique is introduced into the system to cancel both the MUI and the NLD. It is performed at the receiver of the central station where the random ingredients of all user signals are estimated, and the MUI and the NLD are rebuilt and removed from the received signal. The validity of the cancellation technique is theoretically analyzed and shown by the numerical results. The analytical method and its results are also applicable to other general nonlinear CDMA.

In this paper we consider multiuser detection using a neural network in a synchronous code-division multiple-access channel. In a code-division multiple-access channel, a matched filter is widely used as a receiver. However, when the relative powers of the interfering signals are large, i.e. the near-far problem, the performances of the matched filter receiver degrade. Although the optimum receiver for multiuser detection is superior to the matched filter receiver in such situations, the optimum receiver is too complex to be implemented. A simple technique to implement the optimum multiuser detection is required. Recurrent neural networks which consist of a number of simple processing units can rapidly provide a collectively-computed solution. Moreover, the network can seek out a minimum in the energy function. On the other hand, the optimum multiuser detection in a synchronous channel is carried out by the maximization of a likelihood function. In this paper, it is shown that the energy function of the neural network is identical to the likelihood function of the optimum multiuser detection and the neural network can be used to implement the optimum multiuser detection. Performance comparisons among the optimum receiver, the matched filter one and the neural network one are carried out by computer simulations. It is shown that the neural network receiver has a capability to achieve near-optimum performance in several situations and local minimum problems are few serious.