Multi-objective and constrained optimization for DS-CDMA code design based on the clonal selection principle
This paper proposes two new algorithms based on the clonal selection principle for the design of spreading codes for DS-CDMA. The first algorithm follows a multi-objective approach, generating complex spreading codes with good auto as well as cross-correlation properties. It also enables spreading code design with no restrictions on the number of users or code length. The algorithm maintains a repertoire of codes that are subject to cloning and undergo a process of affinity maturation to obtain better codes. Results indicate that the produced code sets lie very close to the theoretical Pareto front. A second penalty function-based constrained optimization algorithm based on clonal selection is proposed. It is applied to the design of spreading codes with pre-defined power spectral density requirement. The results suggest that the algorithm is capable of lowering significantly, the power spectra at undesired frequencies. Therefore, with the proposed algorithm, a DS-CDMA transmitter can, for the first time, selectively transmit power across the transmission bandwidth and adjust to jammers and other interferers. This study illustrates that using two stages of multi-objective and constrained optimization, using the proposed clonal selection algorithms, is an effective code design strategy.
clonal selection multi-objective optimization artificial immune systems wireless communication cdma