  The IEEE 802.11ac/n introduced frame aggregation technology to accommodate
the growing traffic demand and increase the performance of transmission
efficiency and channel utilization. This is achieved by allowing many packets
to be aggregated per transmission which realized a significant enhancement in
the throughput performance of WLAN. However, it is difficult to efficiently
utilize the benefits of frame aggregation in the downlink MU-MIMO channels as
stations have heterogeneous transmission demands and data transmission rates.
As a result of this, wasted space channel time will occur which degrades
transmission efficiency. In addressing these challenges, the existing studies
have proposed different approaches. However, most of these approaches did not
consider a machine-Learning based optimization solution. The main contribution
of this paper is to propose a machine-learning-based frame size optimization
solution to maximize the system throughput of WLAN in the downlink MU-MIMO
channel. In this approach, the Access Point (AP) performs the maximum system
throughput measurement and collected frame size-system throughput patterns
which contain knowledge about the effects of traffic patterns, channel
conditions, and number of stations(STAs). Based on these patterns,our approach
uses a neural network to correctly model the system throughput as a function of
the system frame size. After training the neural network, we obtain the
gradient information to adjust the frame size. the performance of the proposed
Machine learning(ML) approach is evaluated over the FIFO aggregation algorithm
under the effects of heterogenous traffic patterns for VoIP and video
applications, channel conditions, and number of stations.
