Construction of observable and MDP convolutional codes with good decodable properties by ISO representations

This paper addresses the construction of observable and maximum distance profile convolutional codes over finite fields that exhibit good performance with some available decoding algorithms for convolutional codes. Our construction is based on the use of input/state/output representations and the invariance of certain properties of linear systems under various group actions. This framework allows us to systematically generate new convolutional codes from existing ones while preserving key decoding and distance properties.