ISOMORPHIC SIGNAL ENSEMBLES AND THEIR APPLICATION IN ASYNC-ADDRESS SYSTEMS
The object of consideration is async-address systems using code division of subscribers. The subject of the analysis is quasi-orthogonal ensembles of signals based on code sequences that have normalized characteristics of cross-correlation functions (CCF) and provide reliable separation of subscribers (objects) when exposed to imitation and signal-like interference. The purpose of the analysis is to create a model and methodology for construction a set of the best code sequences ensembles having the ability to quickly change the instance of the set to counter imitation and signal-like interference. The solution is based on algebraic models of code sequences and their CCF representation.
The article proposes a comprehensive technique to construct signal ensembles set having normalized characteristics of the CCF. The quality of the primary ensemble of code sequences is ensured by the procedure for calculating the CCF optimized in the number of look over options. Optimization is based on the basic properties of the Galois field, in particular, on the Galois fields’ isomorphism property. It provides a significant reduction in calculations when choosing the primary ensemble of code sequences with the specified properties of the CCF. The very choice of the best (largest in size) code sequences ensemble relies on the solution of one of the classical combinatorics problems – searching for maximal clique on a graph. The construction of signals ensembles set having normalized characteristics of the CCF is ensured by the use of special combinatorial procedures and algorithms based on the multiplicative properties of Galois fields. An analysis of the effectiveness of known and proven procedures searching for maximal clique is also performed in this article. The work results will be useful in the design of infocommunication systems using complex signals with a large base and variable structure to provide protection from signal structure research and the effects of imitation and signal-like interference
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