阐述很详细，适合研究生。
Abstract—In Multi-User MIMO networks, receivers decode
multiple concurrent signals using Successive Interference Cancellation
(SIC). With SIC a weak target signal can be deciphered in
the presence of stronger interfering signals. However, this is only
feasible if each strong interfering signal satisfies a signal-to-noiseplus-
interference ratio (SINR) requirement. This necessitates the
appropriate selection of a subset of links that can be concurrently
active in each receiver’s neighborhood; in other words, a subtopology
consisting of links that can be simultaneously active in
the network is to be formed. If the selected sub-topologies are
of small size, the delay between the transmission opportunities
on a link increases. Thus, care should be taken to form a
limited number of sub-topologies. We find that the problem of
constructing the minimum number of sub-topologies such that
SIC decoding is successful with a desired probability threshold,
is NP-hard. Given this, we propose MUSIC, a framework that
greedily forms and activates sub-topologies, in a way that favors
successful SIC decoding with a high probability. MUSIC also
ensures that the number of selected sub-topologies is kept small.
We provide both a centralized and a distributed version of our
framework. We prove that our centralized version approximates
the optimal solution for the considered problem. We also perform
extensive simulations to demonstrate that (i) MUSIC forms a small
number of sub-topologies that enable efficient SIC operations;
the number of sub-topologies formed is at most 17% larger
than the optimum number of topologies, discovered through
exhaustive search (in small networks). (ii) MUSIC outperforms
approaches that simply consider the number of antennas as a
measure for determining the links that can be simultaneously
active. Specifically, MUSIC provides throughput improvements
of up to 4 times, as compared to such an approach, in various
topological settings. The improvements can be directly attributable
to a significantly higher probability of correct SIC based decoding
with MUSIC.
