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The deployment of Small Cell Base Stations (SCBSs) overlaid on existing macro-cellular systems is seen as a key solution for offloading traffic, optimizing coverage, and boosting the capacity of future cellular wireless systems. The next-generation of SCBSs is envisioned to be multi-mode, i.e., capable of transmitting simultaneously on both licensed and unlicensed bands. This constitutes a cost-effective integration of both Wi-Fi and cellular Radio Access Technologies (RATs) that can efficiently cope with peak wireless data traffic and heterogeneous Quality-of-Service requirements. To leverage the advantage of such multimode SCBSs, the authors discuss the novel proposed paradigm of cross-system learning by means of which SCBSs self-organize and autonomously steer their traffic flows across different RATs.
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