The notion of Generalized Hamming weights of block codes has been investigated since the nineties due to its significant role in coding theory and cryptography. In this paper we extend this concept to the context of convolutional codes. In particular, we focus on column distances and introduce the novel notion of generalized column distances (GCD). We first show that the hierarchy of GCD is strictly increasing. We then provide characterizations of such distances in terms of the truncated parity-check matrix of the code, that will allow us to determine their values. Finally, the case in which the parity-check matrix is in systematic form is treated.

In this work we introduce the concept of Unrestricted Generalized Column Distance (UGCD) for convolutional codes. This is the concept equivalent to the generalized Hamming weights for block codes. We show that the hierarchy of UGCD is strictly increasing and show how to compute it of parity-check matrix in general form. We also provide a way to compute it out of a systematic parity-check matrix.

In this work, we adapt the notion of generalized Hamming weight of block codes to introduce the novel concept of generalized column distances for convolutional codes. This can be considered as an extension of the work done in [18] on the generalized Hamming weights for free distance of convolutional codes. We also introduce the concept of Almost-MDP and Near-MDP convolutional code. The problem of constructing convolutional codes with design generalized column distances remains an interesting open problem that requires further research.