  In this paper, we consider noncoherent random linear coding networks (RLCNs)
as a discrete memoryless channel (DMC) whose input and output alphabets consist
of subspaces. This contrasts with previous channel models in the literature
which assume matrices as the channel input and output. No particular
assumptions are made on the network topology or the transfer matrix, except
that the latter may be rank-deficient according to some rank deficiency
probability distribution. We introduce a random vector basis selection
procedure which renders the DMC symmetric. The capacity we derive can be seen
as a lower bound on the capacity of noncoherent RLCNs, where subspace coding
suffices to achieve this bound.
