  A novel scenario-adapted distributed signaling technique in the context of
opportunistic communications is presented in this work. Each opportunistic user
acquires locally sampled observations from the wireless environment to
determine the occupied and available degrees-of-freedom (DoF). Due to sensing
errors and locality of observations, a performance loss and inter-system
interference arise from subspace uncertainties. Yet, we show that addressing
the problem as a total least-squares (TLS) optimization, signaling patterns
robust to subspace uncertainties can be designed. Furthermore, given the
equivalence of minimum norm and TLS, the latter exhibits the interesting
properties of linear predictors. Specifically, the rotationally invariance
property is of paramount importance to guarantee the detectability by
neighboring nodes. Albeit these advantages, end-to-end subspace uncertainties
yield a performance loss that compromises both detectability and wireless
environment's performance. To combat the latter, we tackle the distributed
identification of the active subspace with and without side information about
neighboring nodes' subspaces. An extensive simulation analysis highlights the
performance of distributed concurrency schemes to achieve subspace agreement.
