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Optimized uplink-downlink decoupling in LTE-U networks: An echo state approach

Uplink-downlink decoupling in which users can be associated to different base stations in the uplink and downlink in 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 an optimization problem which jointly 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. Simulation results show that the proposed approach yields significant gains, in terms of total rate, that reach up to 41% and 54%, respectively, compared to Q-learning and nearest neighbor algorithms. The results also show that ESN significantly improves convergence time of up to 17% compared to Q-learning.