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Unified Multiple-Access Performance Analysis of Several Multirate Multicarrier Spread-Spectrum Systems
M. Nasiri-Kenari (PhD.)
R. Nikjah [PhD.]
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A unified multiple-access performance analysis and comparison of three multicarrier spread-spectrum multiple-access schemes, namely, MC CDMA (Multicarrier Code-Division Multiple-Access), MC-FH (Multicarrier Frequency Hopping) and a hybrid of the above systems, called DS-MC-FH (Direct Sequence MC-FH), in a multirate environment, where each user can have several multirate services, is provided. In MC-CDMA and MC-FH systems, users and their diverse services are differentiated by means of only one kind of signature code. However, in a DS-MC-FH scheme, different users and different services of the same user are distinguished through the first and second signature codes, respectively. The performance of the above systems are evaluated and compared, using a unified structure in synchronous and asynchronous nonfading and synchronous correlated Rayleigh fading channels, with a Maximum Ratio Combining (MRC) receiver. The near-far effect on the systems' performance is also investigated. The (second) signature in the MC-CDMA (DS-MC-FH)scheme is considered to be either a Pseudo-Noise (PN) sequence or a Walsh code. The authors analyses indicate that MC-CDMA systems with Walsh codes outperform the other schemes in different synchronous and asynchronous channels. DS-MC-FH systems with Walsh codes always surpass MC-FH systems. Furthermore, all of the schemes, except synchronous MC-CDMA systems with Walsh codes, are susceptible to a near-far effect with an MRC receiver. |
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Optimizing Multi-Response Statistical Problems Using a Genetic Algorithm
S.T. Akhavan Niaki (PhD.)
S.H.R. Pasandideh [PhD.]
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In this paper, two methods to solve multi-response statistical problems are presented. In these methods, desirability function, genetic algorithm and simulation methodology are applied. The desirability function is responsible for modeling the multi-response statistical problem, the genetic algorithm tries to optimize the model and, finally, the simulation approach generates the required input data from a simulated system. The methods differ from each other in controlling the randomness of the problem. In the first method, replications control this randomness, while, in the second method, the randomness is controlled by a statistical test. Furthermore, these methods are compared by designed experiments and the results are reported. |
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