## 3 Proposed Approach
This study addressed this trade-off by proposing an adaptive aggregation algorithm that allowed a dynamic adaptive aggregation selection scheme by adopting different aggregation policies (FIFO FA, Equal Frame Size FA, Equal MPDUs Agg FA, and Avg Num MPDUs FA) [10]. The aim was to realize an optimal trade-off between maximizing throughput and minimizing delay. Figure 1 shows a flowchart of the proposed adaptive aggregation algorithm while considering the delay. As shown in Figure 1, once the traffic was generated and buffered, the AP performed the optimal aggregation policy selection under the different channel conditions (SNR = 5, 8, or 20 dB), traffic models (Pareto, Weibull, or fBM), and number of STAs while considering a traffic mix of 75% VoIP and 25% video. Then, the aggregation manager aggregated the data frame of each user while employing the four different aggregation policies depicted in [10], such as Agg1, Agg2, Agg3, and Agg4 in Figure 1, which represented the FIFO FA (baseline approach), Equal Frame Size FA, Equal MPDUs Agg FA, and Avg Num MPDUs FA aggregation policies, respectively. Then, the aggregated data frames of each aggregation policy were transmitted one by one to the receiving STAs (receivers) using the  
Figure 1: Proposed adaptive aggregation algorithm. MU-MIMO technique. The receiving stations received different aggregated frame sizes that were obtained using the different aggregation policies. Then, the performance analyzer in the AP evaluated the performance of each aggregation policy, determined which one provided the minimum system delay, and recorded the optimal aggregation policy and the corresponding minimum delay. This record was then utilized in future operations when a similar channel condition and traffic pattern and number of stations occurred in the system. Therefore, from all possible frame sizes obtained from the different aggregation policies, the optimal system frame size was the one obtained from the optimal aggregation policy with the minimum delay. However, if the AP determined the optimal aggregation policy with the given traffic pattern, channel condition, and number of competing stations by retrieving the performance analyzer, frame aggregation could be constructed by the optimal aggregator and the MU-MIMO transmission could be made promptly, as shown in the flowchart. In this study, delay was defined as the average time to successfully transmit a data frame from the source station (AP) to the destination station, while the minimum average delay was the average delay under saturation conditions in the wireless network. As discussed, the aggregation process began after the arrival of a certain numbered frame in the buffer. Moreover, the different aggregation policies adopted different aggregation strategies when selecting frames for aggregation (FIFO FA (baseline approach), Equal Frame Size FA, Equal MPDUs Agg FA, and Avg Num MPDUs FA). Thus, in this study, we considered the queue delay while considering both the waiting delay and aggregation delay. Queue delay (_QDelay_) was defined as the sum of time spent waiting for more packets to arrive (waiting delay) and waiting time for aggregation (aggregation delay), which was the amount of time frames waited in the buffer when they were not selected for aggregation. The frame-aggregation delay was the amount of time between the arrival time of the first frame in the buffer until the time it could start to be transmitted [8]. Equations (1)-(3) illustrate how the queue delay was computed in this study. Both the waiting delay and aggregation delay in the equations considered the time difference between the arrival time of the old frame and just-arrived frames for \(n\) number of stations in the downlink MU-MIMO channel streams. The maximum number of stations assumed in this study was 4.