high overhead in terms of bytes that can be higher than the actual payload. To amortize these overheads, which include the medium access control (MAC) and physical (PHY) headers, acknowledgments (ACK), backoff time, and interframe spacing, the standard also introduced a frame-aggregation scheme that has contributed to high data throughput by combining multiple frames, also known as MAC service data units (MSDUs), into a single transmission unit [1]. The performance of WLAN depends on different performance factors at different layers of the network protocol stack, such as at the PHY and MAC layers. For instance, the frequency channel, modulation and coding schemes, transmitter power, etc., at the PHY layer; and the retry limit, frame size, contention window size, maximum number of backoffs, etc., at the MAC layer have a significant impact on the performance of WLAN. Optimizing these parameters would improve the system performance of WLAN. If a wireless frame size is large, a bit error can destroy the whole frame, thus the frame success rate decreases, and thus the throughput performance is degraded [3]. On the other hand, if the frame size is shorter, the overhead frames such as MAC and PHY headers occupy a large portion of the transmitted frame and thus degrade the transmission efficiency [3]. The IEEE 802.11 standard specifies a constant-length aggregation strategy regardless of the traffic pattern and channel conditions in the actual network. This contributes to the reduction in channel access overhead. However, utilizing the maximum aggregation size may not be optimal in all situations because it may lead to an increase in the delivery of error frames and retransmissions [4]. However, both throughput and delay are the most important performance metrics that need to be considered in frame-aggregation algorithms [5]. Frame aggregation allows protocol overhead to be reduced, thus it can significantly improve throughput performance, but this improvement will also have a related cost in terms of increasing the delay [5; 6; 7]. The time for which more frames wait before transmission in a buffer is the main factor of delay when using frame-aggregation algorithms [5; 6; 7; 8; 9]. Therefore, this indicates that there is a trade-off between increasing throughput and increasing delay. Network delay is an important performance characteristic of the IEEE 802.11 wireless network, and it is usually categorized into different parts such as processing delay, transmission delay, queue delay, and propagation delay [5; 7]. In this study, queue delay and transmission delay were considered. Queue delay is defined as the queuing time of the first arrival packet waiting in the buffer [7; 8]. In this study, it was computed by considering the arrival time of the first frame (i.e., the time from when it arrived at the AP's buffer until the time it was begun to be transmitted) [7; 8]. Transmission delay is defined as the time when a station begins to check the channel state (i.e., idle or busy) for transmitting a frame until it receives an ACK of the frame [6]. Average minimum delay is defined as the average delay under saturation conditions [5; 6]. Under the conditions of an unpredictable heterogeneous traffic pattern and channel conditions, different aggregation algorithms provide different performances in terms of throughput and delay. In addressing these challenges, this study proposed to realize the optimal system throughput of WLAN in a downlink MU-MIMO channel for minimizing system delay by employing a dynamic adaptive aggregation selection scheme. Therefore, the main contribution of this paper was to propose an adaptive aggregation algorithm that attempted to achieve an optimal trade-off between maximizing throughput and minimizing delay. The proposed approach adopted different aggregation policies [10] to adaptively select the optimal aggregation policy that maximized the system throughput while minimizing the cost of delay.