In this paper, we investigate the problem of achieving global optimization for distributed carrier aggregation (CA) in small cell networks, using a game theoretic solution. To cope with the local interference and the distinct cost of intra-band and inter-band CA, we propose a non-cooperation game which is proved as an exact potential game. Furthermore, we propose a spatial adaptive play learning algorithm with heterogeneous learning parameters to converge towards NE of the game. In this algorithm, heterogeneous learning parameters are introduced to accelerate the convergence speed. It is shown that with the proposed game-theoretic approach, global optimization is achieved with local information exchange. Simulation results validate the effectivity of the proposed game-theoretic CA approach.