  Distributed spectrum access (DSA) is challenging since an individual
secondary user often has limited sensing capabilities only. One key insight is
that channel recommendation among secondary users can help to take advantage of
the inherent correlation structure of spectrum availability in both time and
space, and enable users to obtain more informed spectrum opportunities. With
this insight, we advocate to leverage the wisdom of crowds, and devise social
recommendation aided DSA mechanisms to orient secondary users to make more
intelligent spectrum access decisions, for both strong and weak network
information cases. We start with the strong network information case where
secondary users have the statistical information. To mitigate the difficulty
due to the curse of dimensionality in the stochastic game approach, we take the
one-step Nash approach and cast the social recommendation aided DSA decision
making problem at each time slot as a strategic game. We show that it is a
potential game, and then devise an algorithm to achieve the Nash equilibrium by
exploiting its finite improvement property. For the weak information case where
secondary users do not have the statistical information, we develop a
distributed reinforcement learning mechanism for social recommendation aided
DSA based on the local observations of secondary users only. Appealing to the
maximum-norm contraction mapping, we also derive the conditions under which the
distributed mechanism converges and characterize the equilibrium therein.
Numerical results reveal that the proposed social recommendation aided DSA
mechanisms can achieve superior performance using real social data traces and
its performance loss in the weak network information case is insignificant,
compared with the strong network information case.
