## 3 Proposed Approach  
This section proposes a frame-aggregation scheme that allows an adaptive trade-off between maximizing throughput and minimizing delay by employing a dynamic adaptive aggregation selection mechanism for the WLAN downlink MU-MIMO channel. In this study, the challenges of heterogenous traffic patterns, channel errors, and the number of stations, which severely affect the performances of both throughput and delay, were considered. Any improvement achieved by adopting aggregation experiences an increasing delay. Therefore, the adaptive aggregation mechanism is one of the methods to control the performance of throughput and delay. This study proposed to enhance the performance of the adaptive aggregation algorithm in [11] by considering the issues of queue delay and transmission delay, which have a significant impact when frame-aggregation algorithms are adopted. The main contribution of this study was to improve the system throughput of WLAN with the least cost in terms of a delay increase. Generating traffic was the first operation achieved by using Preto, Weibull, and fBM traffic models adopted from [10] for the MAC service data unit (MSDU) data frames in bytes. According to our assumption, constant frame sizes of 100 bytes for a traffic mix of 75% VoIP and 1000 bytes for 25% video traffic [10] were considered. All user frames were buffered in the AP, including the new arrival frame and remaining frames that were not selected for aggregation. Remaining frames could occur due to the target aggregate frame size determined by the type of aggregation policy employed and frames that remained in the buffer due to the maximum A-MPDU frame-size limitation specified in the IEEE 801.11 ac standard [10]. AP buffer frames as large as the maximum storage capacity of 50 MB were allowed. However, if the AP buffer was full, the reception of new frames was denied, and the AP continued with the output process until some space became free at the buffer. Different aggregation algorithms experience different performances in terms of throughput and delay mainly due to unpredictable traffic patterns and channel conditions in the actual network. Thus, the different aggregation policies proposed in [10] experience different performance in terms of both throughput and delay. For instance, FIFO FA allows a maximum frame-size aggregation as long as the maximum aggregate frame size allowed by IEEE 802.11, which improves the throughput. Moreover, it reduces the queue delay of frames before transmission. However, a longer frame size will incur a longer transmission delay. Equal Frame Size FA allows all streams to have an equal aggregation size while considering the station's queue size. This approach always selects the smaller queue size to assemble the target aggregate frame size. Therefore, if the traffic is highly bursty; i.e., if the queue size of the stations has a high variation, the aggregation overhead will be higher due to shorter payload frame aggregation. However, on the contrary, the shorter frame-size aggregation could perform better in error-prone channel conditions [11]. The Equal MP-DUs Agg FA allows an equal number of aggregated frames to be assembled in all streams. Similarly, this aggregation policy is affected by the traffic pattern; i.e., if the traffic is highly bursty, the target aggregate frame size will be shorter, which degrades the throughput and likewise increases the queue delay due to remaining frames left in the buffer before transmission. The Avg Num MPDUs FA considers the average number of frames among streams to assemble the target aggregate frame size. This approach can perform better when the traffic variation among streams is bursty, but it also experiences a delay due to the remaining frames in the buffer. Therefore, this indicates that a trade-off exists between maximizing throughput and minimizing delay when adopting frame-aggregation algorithms due to traffic patterns and channel condition.