Selecting the Most Efficient Reed Solomon Codes to Eliminate Jamming

摘要

Reed-Solomon (RS) codes are, perhaps, the most widely applied channel codes in practice due to its ability to detect and correct random and burst errors. In today's military combat environment, the ability to communicate determines who wins or losses. For this reason electronic warfare (EW) and information warfare (IW) have become an area for many discussions and much research. Jamming environments, especially intentional jamming as in military applications, are confronted with the effects of nonrandom errors. The catalog of Reed-Solomon (RS) codes is a rather long one. To select a proper code for a given application, the system designer is compelled to deal with numerous tables, graphs and equations. I have reported the results of designing an artificial neural network (NN) from which one can select the most 'efficient' RS code for a specific application. In this article I present the continuation of my work, in development of an artificial NN for selection of RS codes to eliminate intentional jamming. Student version of the MATLAB Neural Networks Toolbox is used for NN simulation. The Levenberg-Marquardt learning algorithm is used to train the NN. The resultant NN has six inputs, eleven units in the hidden layer, and one unit in the output layer. The output is 'k'. The test data results show the accuracy of selecting the correct code dimension is 98.04%.