  In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment,
calculation, construction, and maintenance of the packet transmission schedules
are not defined. Moreover, to ensure optimal throughput, most of the existing
scheduling methods are based on the assumption that instantaneous and accurate
Channel State Information (CSI) is available. However, due to the inevitable
errors in the channel estimation process, this assumption cannot be
materialized in many practical scenarios. In this paper, we propose two
alternative and realistic approaches. In our first approach, we assume that
only the statistical knowledge of CSI is available a priori. Armed with this
knowledge, the average packet rate on each link is computed and then, using the
results, the throughput-optimal schedule for the assignment of (slot-frame)
cells to links can be formulated as a max-weight bipartite matching problem,
which can be solved efficiently using the well-known Hungarian algorithm. In
the second approach, we assume that no CSI knowledge (even statistical) is
available at the design stage. For this zero-knowledge setting, we introduce a
machine learning-based algorithm by formally modeling the scheduling problem in
terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based
scheme is widely applicable to many real operational environments, thanks to
its reduced reliance on design-time knowledge. Simulation results show that the
average throughput obtained by the statistical CSI-based method is within the
margin of 15% from the theoretical upper bound associated with perfect
instantaneous CSI. The aforesaid margin is around 18% for our
learning-theoretic solution.
