  Unsourced random access (URA) is an increasingly popular communication
paradigm attuned to machine driven data transfers in
\textit{Internet-of-Things} (IoT) networks. In a typical URA setting, a small
subset of active devices within a very large population wish to transmit short
messages to a central base station. Originally defined for base stations
equipped with a single antenna, the URA paradigm has recently been extended to
practical scenarios involving base stations with a massive number of antennas
by Fengler et al. The proposed concatenated coding scheme therein utilizes a
non-Bayesian sparse recovery algorithm coupled with the tree code introduced by
Amalladinne et al. in the context of coded compressed sensing. Currently, the
existing MIMO implementation uses these two components in isolation. This
article introduces an enhanced successive cancellation list decoding style
scheme that facilitates dynamic interactions between the sparse recovery
algorithm and the tree decoder. This modification can reduce the search space
of the AD algorithm drastically; and it results in significant improvements
both in terms of error performance and computational complexity. Simulation
results reveal that, for a system with 100 active users, the proposed decoder
reduces the number of required antennas at the base station by 23\% to achieve
a performance akin to the scheme by Fengler et al.
