  Uplink-downlink decoupling in which users can be associated to different base
stations in the uplink and downlink of heterogeneous small cell networks (SCNs)
has attracted significant attention recently. However, most existing works
focus on simple association mechanisms in LTE SCNs that operate only in the
licensed band. In contrast, in this paper, the problem of resource allocation
with uplink-downlink decoupling is studied for an SCN that incorporates LTE in
the unlicensed band (LTE-U). Here, the users can access both licensed and
unlicensed bands while being associated to different base stations. This
problem is formulated as a noncooperative game that incorporates user
association, spectrum allocation, and load balancing. To solve this problem, a
distributed algorithm based on the machine learning framework of echo state
networks is proposed using which the small base stations autonomously choose
their optimal bands allocation strategies while having only limited information
on the network's and users' states. It is shown that the proposed algorithm
converges to a stationary mixed-strategy distribution which constitutes a mixed
strategy Nash equilibrium for the studied game. Simulation results show that
the proposed approach yields significant gains, in terms of the sum-rate of the
50th percentile of users, that reach up to 60% and 78%, respectively, compared
to Q-learning and Q-learning without decoupling. The results also show that ESN
significantly provides a considerable reduction of information exchange for the
wireless network.
