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Ant Colony Optimization And Its Application To Multiuser Detection

Posted on:2007-03-09Degree:MasterType:Thesis
Country:ChinaCandidate:Z F YinFull Text:PDF
GTID:2178360182994480Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
It is well known that maximal likelihood detection (MLD) based optimal MUD has been shown to have the exponential computation complexity. So our attention is focusing on the sub-optimal MUD algorithm to solve the difficult issue of MUD design capable of canceling the so-called multiple access interference (MAI) to reach low bit error rate (BER) and high near-far resistant capability with acceptable computation complexity. Now, computational Intelligence (CI) has been one important branch of MUD questions. This thesis is dedicated to the application of ant colony optimization (ACO) to solve the issue of MUD.The main contribution of this thesis can be summarized as follows:(1) A model of MUD based on ACO is firstly proposed in this paper. A simple ACO can be found in this model and it can be easily applied to parallel computing.(2) A new multiuser detector based on rank-based version of ant system (ASrank is proposed . Under analysis the imperfections of the ASrank , we combine evolutionary programming (EP) with ASrank and get a hybrid algorithm. The new algorithm extends the space of searching and reduce the probability of sinking into local minimum.(3) Max-min Ant System (MMAS) is applied to MUD and then combined with tabu search (TS). The results from simulation on computer showed that this method is more effective.multiuser detection (MUD);computational Intelligence(CI);evolutionary programming (EP);tabu search (TS);ant colony optimization (ACO)...
Keywords/Search Tags:multiuser detection (MUD), computational Intelligence(CI), evolutionary programming (EP), tabu search (TS), ant colony optimization (ACO)
PDF Full Text Request
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