Large-kernel Matrix Search Scheme Based on Improved Ant Colony Algorithm
Compared to polar codes with smaller kernel matrix dimensions,large-kernel polar codes usually have larger polarization rates and therefore have better decoding performance.However,with the increase of dimension,the search space of the kernel matrix and the computational complexity of the polarization rate increase exponentially,but the existing research cannot get rid of the exponential complexity brought by the increase of dimension.For the first time,an intelligent optimization algorithm,the ant colony algorithm,is introduced to search for large-kernel matrices with large polarization rate,and by appropriately adjusting the parameters of the algorithm,a superior large-kernel matrix could be found within a feasible time.In addition,Lévy flight is introduced to prevent the algorithm from falling into local optimum too early.Experimental results indicate that the proposed algorithm can stably find out kernel matrices with the best polarization rates of order 13 or less,and can also output good results for matrices of higher dimensions.
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