## 5 Conclusions and Future Works  
The release of the new IEEE 802.11 standards such as IEEE 802.11 ax and IEEE 802.11 ay, 5G technologies, and the massive amount of traffic with mixed types of applications generated in modern networks demand different network performance requirements in terms of maintaining some form of an optimal trade-off between maximizing throughput and minimizing delay. However, the majority of existing researchers only attempted to maximize the throughput or minimize the delay. Both the performance of throughput and delay can be affected by several factors such as heterogeneous traffic pattern, target aggregate frame size, channel condition, competing stations, etc. However, under the effects of uncertain conditions of heterogeneous traffic patterns and channel conditions in a network, determining the optimal target aggregate frame size is significant and can be controlled to manage both the throughput and delay. The main contribution of this paper was to propose an adaptive aggregation algorithm aiming to maximize the system throughput performance of a WLAN in the downlink MU-MIMO channel by maintaining  
Figure 6: Performance of system throughput versus average offered traffic load when NumSTAS = 4 and SNR = 8 dB for the Weibull, Pareto, and fBM traffic models.  
the lowest cost in terms of increasing the system delay. Different aggregation algorithms such as FIFO FA, Equal Frame Size FA, Equal MPDUs Agg FA, and Avg Num MPDUs FA were adopted to achieve the dynamic adaptive aggregation selection scheme to realize an optimal trade-off between maximizing throughput and minimizing delay. The effects of channel conditions, heterogeneous traffic patterns for VoIP and video traffic applications, and number of stations were considered when evaluating the proposed approach. Through a simulation, the performance of the proposed approach was evaluated under various channel conditions, traffic patterns with a traffic mix of VoIP and video, and number of STAs. According to the results, the proposed approach achieved a significant performance in terms of both system throughput and system delay over the FIFO FA (baseline approach), and also achieved a better performance closest to the Adaptive FA Conv. approach, which only focuses on achieving the maximum throughput performance with a cost of maximum delay, than the nonadaptive FIFO FA (baseline approach).  
Future work will extend the proposed adaptive aggregation approach to operate in real traffic scenarios. We will study the effects of different channel models such as Rayleigh and Rician on both uplink and downlink WLAN channels. Moreover, we will evaluate our approach on an experimental testbed.  
Conceptualization, L.K. and J.D.; Data curation, L.K.; Investigation, L.K.; Methodology, L.K.; Software, L.K.; Supervision, M.D., J.D. and J.C.; Validation, M.D. and J.D.; Writing--original draft, L.K.; Writing--review and editing, M.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data are available to any researcher upon request.  
We would like to thank all authors for their contributions and for the success of this manuscript. Moreover, we would like to thank the editors and anonymous reviewers of this manuscript.  
The authors declare that there is no potential conflict of interest concerning the research, authorship, and/or publication of this article.