Multi-hop communication has attracted a lot of
attention recently due to its ability to extend the coverage range
and to overcome blockage. In this letter, we propose a machine
learning-based selection approach that adaptively chooses the
best forwarding scheme in hybrid multi-hop dense networks. The
proposed transmission scheme employs a multi-layer selection
where each layer represents one possible relaying case, namely
amplify-and-forward, half-detection, or full-detection of the
transmitted symbol, or even no-relaying. Moreover, the proposed
system dynamically learns the proper forwarding scheme out of
these layers for each involved relay to minimize the transmission
error rate based on the relay location, and its residual energy.
A heuristic approach is proposed for the forwarding scheme
selection and transmission power control where a minimum
threshold value for the transmission power of each relay node is
derived in order to satisfy a target QoS requirement. The results
are used for training a decision trees-based prediction model
that achieves a remarkable accuracy beyond the 99% for both
training and testing patterns.