A stochastic multi-channel spectrum access game with incomplete information

To ensure continuous functioning and satisfactory performance, a wireless communication system has to not only learn and adapt to the unknown and ever-changing wireless environment, but also strategically deal with the usually unfamiliar peers. Incomplete information stochastic game (SG) is a promising model for the corresponding analysis and strategy design. In this work, an exemplary multi-channel spectrum access game (SAG) with unknown environment dynamics and limited information of the other player is considered to illustrate the proposed solution for the corresponding incomplete information SG. To find the best communication strategy in the face of uncertainty, a joint reinforcement learning and type identification algorithm is developed, which is provably convergent under certain technical conditions. Numerical results show that using the proposed algorithm, a wireless user can gradually achieve the same performance as that in the corresponding complete information game.