Optical code division multiple access (CDMA)-based networks are an interesting alternative to support various traffic types of multimedia applications with highly variable performance targets. Generally, multilength codes are designed to support multirate services, while the multiweight codes are designed to support differentiated quality of service (QoS) for multimedia applications. However, existing optical orthogonal codes (OOCs) are limited to single class or multiclass with restricted weight and length properties. Therefore, there exists a lack of flexibility in the existing OOCs to support arbitrary rate and QoS. This paper presents a proposal of generation procedure and performance analysis of joint multiweight multilength strict OOCs. The approach used in this paper is to apply a methodology strongly relying on developed analytical theory that is supported by computer optimization, because it has turned out that it is mathematically intractable to construct unconstraint joint multilength multiweight OOCs using pure algebraic techniques. The generated code set fulfills the conditions of strictly OOCs, namely, the maximum nonzero shift autocorrelation and the maximum cross correlation constraints of one. The mark position difference (MPD) approach is used to generate in a flexible way the multiclass code set. The MPD results in the simple evaluation of multiclass code set cardinality. Furthermore, the multiple-access interference (MAI) in a multiclass OOC system is evaluated by modeling the interference per class as a Poisson distribution to simplify performance evaluation with acceptable accuracy.
The deterioration and deformation of a free-space optical beam wave-front as it propagates through the atmosphere can reduce the link availability and may introduce burst errors thus degrading the performance of the system. We investigate the suitability of utilizing soft-computing (SC) based tools for improving performance of free-space optical (FSO) communications systems. The SC based tools are used for the prediction of key parameters of a FSO communications system. Measured data collected from an experimental FSO communication system is used as training and testing data for a proposed multi-layer neural network predictor (MNNP) used to predict future parameter values. The predicted parameters are essential for reducing transmission errors by improving the antenna's accuracy of tracking data beams. This is particularly essential for periods considered to be of strong atmospheric turbulence. The parameter values predicted using the proposed tool show acceptable conformity with original measurements.
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