Network slicing offers the potential to enhance service satisfaction and
optimize resource utilization, particularly in scenarios where radio
resources are limited. Subslicing has been shown to improve the slice
performance. In this paper, the monitor-analyze-plan-execute-knowledge
(MAPE-K)-type management closed control loop (MCCL) is implemented for
slice performance improvement by subslicing. The subslicing can improve
slice performance if the slice performance depends on the size of slice
bandwidth part (BWP). For Plan function, the classifier neural network
was trained to decide whether the subslice should be split, merged or
not changed by their performance. The training data contains slice
performance data of all possible subslice sizes. For Execute function,
the subslice splitting algorithm was proposed, which clusters UEs by
their block error ratio (BLER) and allocates bandwidth proportionally to
group requested sum rate and group BLER. A realistic 5G new radio (NR)
band serving a set of user equipments (UE) of different values of their
BLER and requested rates was a setup of radio access network (RAN) slice
simulated using MATLAB R2021b. Subslicing has reduced bandwidth
utilization, and slice BLER while increased slice goodput
(application-level throughput). Proposed subslice splitting algorithm
when UEs are clustered by their achieved BLER, then the slice BLER
reduces additional 20% and slice goodput increases up to additional 9%
compared to no subslicing when UEs are clustered by their requested
rates. This effect was larger for the uplink. In runtime scenarios for
poor-BLER UEs the smaller subslices improve slice utilization and BLER,
while larger subslices improve goodput.